Each "model" is a specific parameterization of an interatomic model class for a given material system (e.g. the Lennard-Jones potential for Ar). Click for more information.
Choose from the tab above to sort the models in different ways.
When sorting by species, you can narrow the selection to find potentials that support multiple species.
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_ADP_ApostolMishin_2011_AlCu__SM_667696763561_000 | adp | LAMMPS ADP potential for Al-Cu developed by Apostol and Mishin (2011) v000 |
| Sim_LAMMPS_ADP_HowellsMishin_2018_Cr__SM_884076133432_000 | adp | LAMMPS ADP potential for Cr developed by Howells and Mishin (2018) v000 |
| Sim_LAMMPS_ADP_MishinMehlPapaconstantopoulos_2005_Ni__SM_477692857359_000 | adp | LAMMPS ADP Potential for Ni developed by Mishin et al. (2005) v000 |
| Sim_LAMMPS_ADP_PunDarlingKecskes_2015_CuTa__SM_399364650444_000 | adp | LAMMPS ADP potential for the Cu-Ta system developed by Pun et al. (2015) v000 |
| Sim_LAMMPS_ADP_SmirnovaStarikov_2017_ZrNb__SM_937902197407_000 | adp | LAMMPS ADP potential for the Zr-Nb system developed by Smirnova and Starikov (2017) v000 |
| Sim_LAMMPS_ADP_SmirnovaStarikovVlasova_2018_MgH__SM_899925688973_000 | adp | LAMMPS ADP potential for the Mg-H system developed by Smirnova, Starikov and Vlasova (2018) v000 |
| Sim_LAMMPS_ADP_StarikovGordeevLysogorskiy_2020_SiAuAl__SM_113843830602_000 | adp | LAMMPS ADP potential for the Si-Au-Al system developed by Starikov et al. (2020) v000 |
| Sim_LAMMPS_ADP_StarikovKolotovaKuksin_2017_UMo__SM_682749584055_000 | adp | LAMMPS ADP potential for the U-Mo system developed by Starikov et al. (2017) v000 |
| Sim_LAMMPS_ADP_StarikovLopanitsynaSmirnova_2018_SiAu__SM_985135773293_000 | adp | LAMMPS ADP potential for the Si-Au system developed by Starikov et al. (2018) v000 |
| Sim_LAMMPS_ADP_StarikovSmirnova_2021_ZrNb__SM_993852507257_000 | adp | LAMMPS ADP potential for the Zr-Nb system developed by Starikov and Smirnova (2021) v000 |
| Sim_LAMMPS_ADP_StarikovSmirnovaPradhan_2021_Fe__SM_906654900816_000 | adp | LAMMPS ADP potential for Fe developed by Starikov et al. (2021) v000 |
| Sim_LAMMPS_ADP_TseplyaevStarikov_2016_UN__SM_474015477315_000 | adp | LAMMPS ADP potential for the U-N system developed by Tseplyaev and Starikov (2016) v000 |
| Sim_LAMMPS_ADP_WangXuQian_2021_AuRh__SM_066295357485_000 | adp | LAMMPS ADP potential for the Au-Rh system developed by Wang et al. (2021) v000 |
| Sim_LAMMPS_ADP_XuWangQian_2022_NiPd__SM_559286646876_000 | adp | LAMMPS ADP potential for the Ni-Pd system developed by Xu et al. (2022) v000 |
| Sim_LAMMPS_ADP_XuWangQian_2022_NiRh__SM_306597220004_000 | adp | LAMMPS ADP potential for the Ni-Rh system developed by Xu et al. (2022) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_AGNI_BotuBatraChapman_2017_Al__SM_666183636896_000 | agni | LAMMPS AGNI potential for Al developed by Botu et al. (2017) v000 |
| Sim_LAMMPS_AGNI_BotuRamprasad_2015_Al__SM_526060833691_000 | agni | LAMMPS AGNI potential for Al developed by Botu and Ramprasad (2015) v000 |
| Model | Type | Title |
|---|---|---|
| model_ArCHHeXe_BOP_AIREBO__MO_154399806462_001 | airebo | AIREBO reactive potential for carbon and hydrocarbon systems |
| Sim_LAMMPS_AIREBO_LJ_StuartTuteinHarrison_2000_CH__SM_069621990420_000 | airebo | LAMMPS AIREBO-LJ potential for C-H developed by Stuart, Tutein, and Harrison (2000) v000 |
| Sim_LAMMPS_AIREBO_Morse_OConnorAndzelmRobbins_2015_CH__SM_460187474631_000 | airebo | LAMMPS AIREBO-M potential for C-H developed by O'Connor, Andzelm, and Robbins (2015) v000 |
| Model | Type | Title |
|---|---|---|
| ThreeBodyCluster_BH_BiswasHamann_1987_Si__MO_019616213550_000 | bh | Three-body cluster potential for Si by Biswas and Hamann (1987) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_BOP_MurdickZhouWadley_2006_GaAs__SM_104202807866_001 | bop | LAMMPS BOP potential for the Ga-As system developed by Murdick et al. (2006) v001 |
| Sim_LAMMPS_BOP_WardZhouWong_2012_CdTe__SM_509819366101_001 | bop | LAMMPS BOP potential for the Cd-Te system developed by Ward et al. (2012) v001 |
| Sim_LAMMPS_BOP_WardZhouWong_2012_CdZnTe__SM_409035133405_001 | bop | LAMMPS BOP potential for the Cd-Zn-Te system developed by Ward et al. (2012) v001 |
| Sim_LAMMPS_BOP_WardZhouWong_2013_CdZnTe__SM_010061267051_000 | bop | LAMMPS BOP potential for the Cd-Zn-Te system developed by Ward et al. (2013) v000 |
| Sim_LAMMPS_BOP_ZhouFosterVanSwol_2014_CdTeSe__SM_567065323363_000 | bop | LAMMPS BOP potential for the Cd-Te-Se system developed by Zhou et al. (2014) v000 |
| Sim_LAMMPS_BOP_ZhouWardFoster_2015_CCu__SM_784926969362_000 | bop | LAMMPS BOP potential for the C-Cu system developed by Zhou, Ward, and Foster (2015) v000 |
| Sim_LAMMPS_BOP_ZhouWardFoster_2015_CuH__SM_404135993060_000 | bop | LAMMPS BOP potential for the Cu-H system developed by Zhou et al. (2015) v000 |
| Sim_LAMMPS_BOP_ZhouWardFoster_2016_AlCu__SM_566399258279_001 | bop | LAMMPS BOP potential for the Al-Cu system developed by Zhou, Ward, and Foster (2016) v001 |
| Sim_LAMMPS_BOP_ZhouWardFoster_2018_AlCuH__SM_834012669168_000 | bop | LAMMPS BOP potential for the Al-Cu-H system developed by Zhou, Ward and Foster (2018) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_Buckingham_ArimaYamasakiTorikai_2005_CeO__SM_328512278696_000 | buckingham | LAMMPS Buckingham potential for CeO2 developed by Arima et al (2005) v000 |
| Sim_LAMMPS_Buckingham_ArimaYoshidaMatsumoto_2014_PuUThNpO__SM_182981756100_000 | buckingham | LAMMPS Buckingham potential for MOX oxides developed by Arima et al (2014) v000 |
| Sim_LAMMPS_Buckingham_CarreHorbachIspas_2008_SiO__SM_886641404623_000 | buckingham | LAMMPS Buckingham potential for SiO2 developed by Carré et al. (2008) v000 |
| Sim_LAMMPS_Buckingham_FangKeltyHe_2014_LaO__SM_576027677976_000 | buckingham | LAMMPS Buckingham potential for La2O3 developed by Fang et al (20014) v000 |
| Sim_LAMMPS_Buckingham_FisherMatsubara_2005_NiO__SM_337243826931_000 | buckingham | LAMMPS Buckingham potential for NiO developed by Fisher and Matsubara (2005) v000 |
| Sim_LAMMPS_Buckingham_FreitasSantosColaco_2015_SiCaOAl__SM_154093256665_000 | buckingham | LAMMPS Buckingham potential for CaO–Al2O3–SiO2 systems developed by Freitas et al. (2015) v000 |
| Sim_LAMMPS_Buckingham_GhoshSomayajuluArya_2015_ThCeO__SM_681317476351_000 | buckingham | LAMMPS Buckingham potential for (Th,Ce)O2 mized oxides developed by Ghosh et al (2005) v000 |
| Sim_LAMMPS_Buckingham_MatsuiAkaogi_1991_TiO__SM_690504433912_000 | buckingham | LAMMPS Buckingham potential for TiO2 developed by Matsui and Akaogi (1991) v000 |
| Sim_LAMMPS_Buckingham_MomenzadehBelovaMurch_2021_ZrYO__SM_376275128969_000 | buckingham | LAMMPS Buckingham potential for yttria-stabilized zirconia by Momenzadeh et al (2021) v000 |
| Sim_LAMMPS_Buckingham_SayleCatlowMaphanga_2005_MnO__SM_757974494010_000 | buckingham | LAMMPS Buckingham potential for MnO2 developed by Sayle et al. (2005) v000 |
| Sim_LAMMPS_Buckingham_SunStirnerHagston_2006_AlO__SM_466046725502_000 | buckingham | LAMMPS Buckingham potential for a-Al2O3 developed by Sun et al. (2006) v000 |
| Sim_LAMMPS_Buckingham_SunStirnerHagston_2006_MgO__SM_152356670345_000 | buckingham | LAMMPS Buckingham potential for MgO developed by Sun et al. (2006) v000 |
| Sim_LAMMPS_Buckingham_Vaari_2015_FeO__SM_672759489721_000 | buckingham | LAMMPS Buckingham potential for a-Fe2O3 (hematite) reported by Vaari (2015) v000 |
| Sim_LAMMPS_Buckingham_WangShinShin_2019_CrO__SM_295921111679_000 | buckingham | LAMMPS Buckingham potential for Cr2O3 reported by Wang, Shin and Shin (2019) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_IFF_CHARMM_GUI_HeinzLinMishra_2023_Nanomaterials__SM_232384752957_000 | charmm | Interface Force Field (IFF) parameters due to Heinz et al. as used in the CHARMM-GUI input generator v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_IFF_PCFF_HeinzMishraLinEmami_2015Ver1v5_FccmetalsMineralsSolventsPolymers__SM_039297821658_001 | class2 | LAMMPS PCFF bonded force-field combined with IFF non-bonded 9-6 Lennard-Jones potentials for metal interactions v001 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_Buckingham_PotashnikovBoyarchenkovNekrasov_2011_PuUO__SM_422015835006_000 | comb | LAMMPS Buckingham potential for (U,Pu)O2 materials developed by Potashnikov et al (2011) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_CoreShell_MitchellFincham_1993_CaF__SM_676649151762_000 | cs | LAMMPS adiabatic core-shell model for the Ca-F system developed by Mitchell and Fincham (1993) v000 |
| Sim_LAMMPS_CoreShell_MitchellFincham_1993_MgO__SM_579243392924_000 | cs | LAMMPS adiabatic core-shell model for the Mg-O system developed by Mitchell and Fincham (1993) v000 |
| Sim_LAMMPS_CoreShell_MitchellFincham_1993_NaCl__SM_672022050407_000 | cs | LAMMPS adiabatic core-shell model for the Na-Cl system developed by Mitchell and Fincham (1993) v000 |
| Model | Type | Title |
|---|---|---|
| DUNN_Gupta_2026N1_Si__MO_246333365926_000 | dunn | Neural Network potential for Si by Gupta (2026) N=1 (in series of 3) v000 |
| DUNN_Gupta_2026N2_Si__MO_433917300633_000 | dunn | Neural Network potential for Si by Gupta (2026) N=2 (in series of 3) v000 |
| DUNN_Gupta_2026N3_Si__MO_798099350634_000 | dunn | Neural Network potential for Si by Gupta (2026) N=3 (in series of 3) v000 |
| DUNN_WenTadmor_2019v1_C__MO_584345505904_001 | dunn | Dropout uncertainty neural network (DUNN) potential for condensed-matter carbon systems developed by Wen and Tadmor (2019) v001 |
| DUNN_WenTadmor_2019v2_C__MO_956135237832_001 | dunn | Dropout uncertainty neural network (DUNN) potential for condensed-matter carbon systems developed by Wen and Tadmor (2019) v001 |
| DUNN_WenTadmor_2019v3_C__MO_714772088128_001 | dunn | Dropout uncertainty neural network (DUNN) potential for condensed-matter carbon systems developed by Wen and Tadmor (2019) v001 |
| Model | Type | Title |
|---|---|---|
| EAM_CubicNaturalSpline_AngeloMoodyBaskes_1995_Ni__MO_800536961967_004 | eam | EAM potential (cubic natural spline tabulation) for Ni developed by Angelo et al. (1995) modified by Dupuy for smooth derivatives v004 |
| EAM_CubicNaturalSpline_ErcolessiAdams_1994_Al__MO_800509458712_003 | eam | EAM potential (cubic natural spline tabulation) for Al developed by Ercolessi and Adams (1994) v003 |
| EAM_Dynamo_Ackland_1987_Au__MO_754413982908_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Ackland et al. (1987) v001 |
| EAM_Dynamo_Ackland_1992_Ti__MO_748534961139_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ti for the hcp-fcc transition developed by Ackland (1992) v006 |
| EAM_Dynamo_Ackland_2003_W__MO_141627196590_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for W developed by Ackland (2003) v006 |
| EAM_Dynamo_AcklandBaconCalder_1997_Fe__MO_142799717516_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for bcc Fe developed by Ackland et al. (1997) v006 |
| EAM_Dynamo_AcklandMendelevSrolovitz_2004_FeP__MO_884343146310_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the Fe-P system developed by Ackland et al. (2004) v006 |
| EAM_Dynamo_AcklandThetford_1987_Mo__MO_789280224186_000 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Mo developed by Ackland and Thetford (1987) v000 |
| EAM_Dynamo_AcklandTichyVitek_1987_Ag__MO_212700056563_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ag developed by Ackland et al. (1987) v006 |
| EAM_Dynamo_AcklandTichyVitek_1987_Au__MO_104891429740_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Au due to Ackland et al. (1987) v006 |
| EAM_Dynamo_AcklandTichyVitek_1987_Cu__MO_179025990738_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Cu developed by Ackland et al. (1987) v006 |
| EAM_Dynamo_AcklandTichyVitek_1987_Ni__MO_977363131043_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ni developed by Ackland et al. (1987) v006 |
| EAM_Dynamo_AcklandTichyVitek_1987v2_Ag__MO_055919219575_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ag developed by Ackland et al. (1987), version 2 refitted for radiation studies v001 |
| EAM_Dynamo_AcklandTichyVitek_1987v2_Cu__MO_762798677854_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Cu developed by Ackland et al. (1987), version 2 refitted for radiation studies v001 |
| EAM_Dynamo_AcklandTichyVitek_1987v2_Ni__MO_769632475533_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ni developed by Ackland et al. (1987), version 2 refitted for radiation studies v001 |
| EAM_Dynamo_AcklandVitek_1990_Cu__MO_642748370624_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Cu developed by Ackland and Vitek (1990) v001 |
| EAM_Dynamo_AcklandWoodingBacon_1995v2_Zr__MO_398441626455_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Zr developed by Ackland et al. (1995), version 2 with short-range repulsion for radiation studies v001 |
| EAM_Dynamo_AdamsFoilesWolfer_1989_Au__MO_087738844640_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au (Universal 4) developed by Adams et al. (1989) v001 |
| EAM_Dynamo_AdamsFoilesWolfer_1989Universal6_Ag__MO_681640899874_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ag (Universal6) developed by Adams, Foiles and Wolfer (1989) v001 |
| EAM_Dynamo_AdamsFoilesWolfer_1989Universal6_Cu__MO_145873824897_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu (Universal6) developed by Adams, Foiles, and Wolfer (1989) v001 |
| EAM_Dynamo_AdamsFoilesWolfer_1989Universal6_Ni__MO_258836200237_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni (Universal6) developed by Adams, Foiles and Wolfer (1989) v001 |
| EAM_Dynamo_AdamsFoilesWolfer_1989Universal6_Pd__MO_169076431435_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pd (Universal6) developed by Adams, Foiles and Wolfer (1989) v001 |
| EAM_Dynamo_AdamsFoilesWolfer_1989Universal6_Pt__MO_388062184209_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pt (Universal6) developed by Adams, Foiles and Wolfer (1989) v001 |
| EAM_Dynamo_AgrawalMishraWard_2013_Be__MO_404563086984_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Be developed by Agrawal et al. (2013) v001 |
| EAM_Dynamo_AlleraRibeiroPerez_2022_FeC__MO_324606345076_000 | eam | EAM Potential developed by Allera et al. to model the effects of C on BCC Fe (2022) v000 |
| EAM_Dynamo_AngeloMoodyBaskes_1995_NiAlH__MO_418978237058_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Al-H system developed by Angelo, Moody and Baskes (1995) v006 |
| EAM_Dynamo_AroraBonnyCastin_2021_FeNiCrPd__MO_408187748449_000 | eam | EAM Potential for Fe, Ni, Cr, Pd, developed by Arora et al. (2021) v000 |
| EAM_Dynamo_BelandLuOsetskiy_2016_CoNi__MO_871937946490_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Co system developed by Beland et al. (2016) v001 |
| EAM_Dynamo_BelandTammMu_2017_FeNiCr__MO_715003088863_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Fe-Ni-Cr system developed by Beland et al. (2017) v001 |
| EAM_Dynamo_BonnyBakaevTerentyev_2017_WRe__MO_234187151804_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the W-Re system developed by Bonny et al. (2017) v001 |
| EAM_Dynamo_BonnyCastinBullens_2013_FeW__MO_737567242631_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Fe-W system developed by Bonny et al. (2013) v001 |
| EAM_Dynamo_BonnyCastinTerentyev_2013_FeNiCr__MO_763197941039_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Fe-Ni-Cr system developed by Bonny, Castin and Terentyev (2013) v001 |
| EAM_Dynamo_BonnyGrigorevTerentyev_2014EAM1_WHHe__MO_292520929154_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the W-H-He system developed by Bonny et al. (2014); Potential EAM1 v001 |
| EAM_Dynamo_BonnyGrigorevTerentyev_2014EAM2_WHHe__MO_626183701337_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the W-H-He system developed by Bonny et al. (2014); Potential EAM2 v001 |
| EAM_Dynamo_BonnyPasianotCastin_2009_FeCuNi__MO_469343973171_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe-Cu-Ni reactor pressure vessel steels developed by Bonny et al. (2009) v006 |
| EAM_Dynamo_BonnyPasianotMalerba_2009_FeNi__MO_267721408934_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the FeNi system developed by Bonny, Pasianot and Malerba (2009) v006 |
| EAM_Dynamo_BonnyTerentyevBakaev_2013_FeCuNiMn__MO_398449950394_000 | eam | EAM Potential for Fe, Cu, Ni, and Mn developed by Bonny, Terentyev, and Bakaev et al. (2013) v000 |
| EAM_Dynamo_BonnyTerentyevPasianot_2011_FeNiCr__MO_677715648236_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Fe-Ni-Cr system developed by Bonny et al. (2011) v001 |
| EAM_Dynamo_BorovikovMendelevKing_2016_CuZr__MO_097471813275_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the Cu-Zr system developed by Borovikov, Mendelev and King (2016) v001 |
| EAM_Dynamo_CaiYe_1996_AlCu__MO_942551040047_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Al-Cu system developed by Cai and Ye (1996) v006 |
| EAM_Dynamo_ChamatiPapanicolaouMishin_2006_Fe__MO_960699513424_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Chamati et al. (2006) v001 |
| EAM_Dynamo_ChenFangLiu_2019_WRe__MO_473237400492_000 | eam | EAM potential (LAMMPS cubic hermite tabulation) for W-Re with improved representations over the 2018 potential, developed by Chen, Li, Gao et al. (2018) v000 |
| EAM_Dynamo_ChenFangLiu_2019_WTa__MO_645806019892_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the W-Ta system developed by Chen et al. (2019) v001 |
| EAM_Dynamo_ChenFangLiu_2021_WTaHe__MO_840023845283_000 | eam | Finnis–Sinclair potential for the ternary W-Ta-He system, developed by Chen, Fang, Liao et al. (2021) v000 |
| EAM_Dynamo_ChenLiaoGao_2020_WMo__MO_455557982718_000 | eam | Finnis-Sinclair potential for W and Mo binary systems developed by Chen, Liao, Gao et al. (2020) v000 |
| EAM_Dynamo_ChenLiaoGao_2020_WV__MO_944355520926_000 | eam | Finnis-Sinclair potential for W and V binary systems developed by Chen, Liao, Gao et al. (2020) v000 |
| EAM_Dynamo_ChenLiGao_2018_WRe__MO_138684956898_000 | eam | EAM potential (LAMMPS cubic hermite tabulation) for W-Re alloys developed by Chen, Li, Gao et al. (2018) v000 |
| EAM_Dynamo_ClementAuger_2022_CuZn__MO_875501152989_000 | eam | EAM potential for α-brass copper–zinc alloys developed by Clement and Auger to model plasticity and fracture (2022) v000 |
| EAM_Dynamo_DaramolaBonnyAdjanor_2022_FeNiCrMn__MO_591022523366_000 | eam | EAM Finnis-Sinclair potential developed by Daramola et al. to model CrFeMnNi quaternary HEAs (2022) v000 |
| EAM_Dynamo_DeluigiPasianotValencia_2021_FeNiCrCoCu__MO_657255834688_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for FeNiCrCoCu developed by Deluigi et al. (2021) v001 |
| EAM_Dynamo_ErcolessiAdams_1994_Al__MO_123629422045_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Ercolessi and Adams (1994) v006 |
| EAM_Dynamo_FarkasCaro_2018_FeNiCrCoCu__MO_803527979660_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Fe-Ni-Cr-Co-Cu system developed by Farkas and Caro (2018) v001 |
| EAM_Dynamo_FarkasCaro_2020_FeNiCrCoAl__MO_820335782779_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Fe-Ni-Cr-Co-Al system developed by Farkas and Caro (2020) v001 |
| EAM_Dynamo_FarkasJones_1996_NbTiAl__MO_042691367780_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Nb-Ti-Al system developed by Farkas and Jones (1996) v001 |
| EAM_Dynamo_FellingerParkWilkins_2010_Nb__MO_102133002179_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Nb developed by Fellinger, Park and Wilkins (2010) v006 |
| EAM_Dynamo_FischerSchmitzEich_2019_CuNi__MO_266134052596_001 | eam | EAM potential for Cu–Ni developed by Fischer et al. (2019) v001 |
| EAM_Dynamo_Foiles_1985_Cu__MO_831121933939_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Foiles (1985) for NiCu alloys v001 |
| EAM_Dynamo_Foiles_1985_Ni__MO_010059867259_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Foiles (1985) for NiCu alloys v001 |
| EAM_Dynamo_FoilesBaskesDaw_1986Universal3_Ag__MO_626948998302_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ag (Universal3) developed by Foiles, Baskes, and Daw (1986) v001 |
| EAM_Dynamo_FoilesBaskesDaw_1986Universal3_Au__MO_559016907324_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au (Universal3) developed by Foiles, Baskes, and Daw (1986) v001 |
| EAM_Dynamo_FoilesBaskesDaw_1986Universal3_Cu__MO_666348409573_005 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu (Universal3) developed by Foiles, Baskes, and Daw (1986) v005 |
| EAM_Dynamo_FoilesBaskesDaw_1986Universal3_Ni__MO_580571659842_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni (Universal3) developed by Foiles, Baskes, and Daw (1986) v001 |
| EAM_Dynamo_FoilesBaskesDaw_1986Universal3_Pd__MO_786012902615_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pd (Universal3) developed by Foiles, Baskes, and Daw (1986) v001 |
| EAM_Dynamo_FoilesBaskesDaw_1986Universal3_Pt__MO_757342646688_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pt (Universal3) developed by Foiles, Baskes, and Daw (1986) v001 |
| EAM_Dynamo_FoilesHoyt_2006_Ni__MO_776437554506_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Foiles and Hoyt (2006) v001 |
| EAM_Dynamo_FortiniMendelevBuldyrev_2008_Ru__MO_114077951467_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ru developed by Fortini et al. (2008) v006 |
| EAM_Dynamo_GolaPastewka_2018_CuAu__MO_426403318662_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu-Au alloys developed by Gola and Pastewka (2018) v001 |
| EAM_Dynamo_GrocholaRusso_2005_Au__MO_557267801129_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Grochola et al. (2005) v001 |
| EAM_Dynamo_HaleWongZimmerman_2008PairHybrid_PdAgH__MO_104806802344_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Pd-Ag-H ternary alloy system developed by Hale et al. (2013) (hybrid Pd-Ag interactions) v006 |
| EAM_Dynamo_HaleWongZimmerman_2008PairMorse_PdAgH__MO_108983864770_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pd-Ag-H ternary alloy system developed by Hale et al. (2013) (Morse Pd-Ag interactions) v006 |
| EAM_Dynamo_HanZepedaAckland_2003_V__MO_411020944797_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for V developed by Han et al. (2003) v001 |
| EAM_Dynamo_HanZepedaAckland_2003_W__MO_286137913440_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for W developed by Han et al. (2003) v001 |
| EAM_Dynamo_HepburnAckland_2008_FeC__MO_143977152728_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the FeC system developed by Ackland and Hepburn (2008) v006 |
| EAM_Dynamo_HoytGarvinWebb_2003_PbCu__MO_119135752160_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Pb-Cu system developed by Hoyt et al. (2003) v006 |
| EAM_Dynamo_JacobsenNorskovPuska_1987_Al__MO_411692133366_001 | eam | EMT potential (LAMMPS cubic hermite tabulation) for Al developed by Karsten, Norskov and Puska (1987) v001 |
| EAM_Dynamo_KumarLudhwaniDas_2023_FeH__MO_680566758384_000 | eam | EAM Potential developed to model the effect of interstitial hydrogen concentration on plasticity in α-Fe by Kumar, Ludhwani, and Das et al. (2023) v000 |
| EAM_Dynamo_LandaWynblattSiegel_2000_AlPb__MO_699137396381_006 | eam | Glue potential (LAMMPS cubic hermite tabulation) for the Al-Pb system developed by Landa et al. (2000) v006 |
| EAM_Dynamo_LiSiegelAdams_2003_Ta__MO_103054252769_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ta developed by Li et al. (2003) v006 |
| EAM_Dynamo_LiuAdams_1998_AlMg__MO_019873715786_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Al-Mg system developed by Liu and Adams (1998) v001 |
| EAM_Dynamo_LiuErcolessiAdams_2004_Al__MO_051157671505_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Liu, Ercolessi and Adams (2004) v001 |
| EAM_Dynamo_LiuLiuBorucki_1999_AlCu__MO_020851069572_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Al-Cu system developed by Liu et al. (1999) v001 |
| EAM_Dynamo_LiuOhotnickyAdams_1997_AlMg__MO_559870613549_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Al-Mg system developed by Liu et al. (1997) v001 |
| EAM_Dynamo_MarescaCurtin_2020_NbTaV__MO_330233188199_001 | eam | EAM potential for the Nb-Ta-V system developed by Maresca and Curtin (2020) v001 |
| EAM_Dynamo_Marinica_2007_Fe__MO_466808877130_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Fe developed by Marinica (2007) v001 |
| EAM_Dynamo_Marinica_2011_Fe__MO_255315407910_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Fe developed by Marinica (2011) v001 |
| EAM_Dynamo_MarinicaVentelonGilbert_2013EAM2_W__MO_204305659515_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for W developed by Marinica et al. (2013); Potential EAM2 v001 |
| EAM_Dynamo_MarinicaVentelonGilbert_2013EAM3_W__MO_706622909913_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for W developed by Marinica et al. (2013); Potential EAM3 v001 |
| EAM_Dynamo_MarinicaVentelonGilbert_2013EAM4__MO_046576227003_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for W developed by Marinica et al. (2013); Potential EAM4 v001 |
| EAM_Dynamo_MasonNguyenManhBecquart_2017_W__MO_268730733493_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for W developed by Mason, Nguyen-Manh, Becquart (2017) v001 |
| EAM_Dynamo_Mendelev_2003_Fe__MO_546673549085_000 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Mendelev et al. (2003) v000 |
| EAM_Dynamo_Mendelev_2007_Zr__MO_848899341753_000 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Zr developed by Mendelev and Ackland (2007) v000 |
| EAM_Dynamo_Mendelev_2015_Na__MO_094065024556_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Na developed by Mendelev (2015) v001 |
| EAM_Dynamo_Mendelev_2018_Tb__MO_522239651961_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Tb developed by Mendelev (2018) v001 |
| EAM_Dynamo_MendelevAckland_2007_Zr__MO_537826574817_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Zr developed by Mendelev and Ackland (2007) v001 |
| EAM_Dynamo_MendelevAckland_2007v3_Zr__MO_004835508849_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Zr developed by Mendelev and Ackland (2007); version 3 refitted for radiation studies v001 |
| EAM_Dynamo_MendelevAstaRahman_2009_AlMg__MO_658278549784_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for solid-liquid interfaces in Al-Mg alloys developed by Mendelev et al. (2009) v006 |
| EAM_Dynamo_MendelevBorovikov_2020_FeNiCr__MO_922363340570_001 | eam | Finnis-Sinclair potential for the Fe-Ni-Cr system developed by Mendelev et al. (2020) v001 |
| EAM_Dynamo_MendelevFangYe_2015_AlSm__MO_338600200739_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the Al-Sm system developed by Mendelev et al. (2015) v001 |
| EAM_Dynamo_MendelevHanSon_2007_VFe__MO_249706810527_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the V-Fe system developed by Mendelev et al. (2007) v006 |
| EAM_Dynamo_MendelevHanSrolovitz_2003_Fe__MO_807997826449_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Mendelev et al. (2003) v001 |
| EAM_Dynamo_MendelevHanSrolovitz_2003Potential2_Fe__MO_769582363439_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Mendelev et al. (2003); Potential #2 v006 |
| EAM_Dynamo_MendelevHanSrolovitz_2003Potential5_Fe__MO_942420706858_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Mendelev et al. (2003); Potential #5 v006 |
| EAM_Dynamo_MendelevKing_2013_Cu__MO_748636486270_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Cu with improved stacking fault energy developed by Mendelv and King (2013) v006 |
| EAM_Dynamo_MendelevKramerBecker_2008_Al__MO_106969701023_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Al developed by Mendelev et al. (2008) v006 |
| EAM_Dynamo_MendelevKramerBecker_2008_Cu__MO_945691923444_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu solidification developed by Mendelev et al. (2008) v006 |
| EAM_Dynamo_MendelevKramerHao_2012_Ni__MO_832600236922_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ni solidification developed by Mendelev et al. (2012) v05 v006 |
| EAM_Dynamo_MendelevKramerHao_2012_NiZr__MO_149104665840_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the Ni-Zr system developed by Mendelev et al. (2012) v006 |
| EAM_Dynamo_MendelevKramerOtt_2009_CuZr__MO_600021860456_006 | eam | Finnis-Sinclar potential (LAMMPS cubic hermite tabulation) for liquid and amorphous Cu-Zr alloys developed by Mendelev et al. (2009) v006 |
| EAM_Dynamo_MendelevSordeletKramer_2007_CuZr__MO_120596890176_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the Cu-Zr system developed by Mendelev, Sordelet and Kramer (2007) v006 |
| EAM_Dynamo_MendelevSrolovitzAckland_2005_AlFe__MO_577453891941_006 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the Al-Fe system developed by Mendelev et al. (2005) v006 |
| EAM_Dynamo_MendelevSunZhang_2019_CuZr__MO_609260676108_001 | eam | Finnis-Sinclair potential for the Cu-Zr system developed by Mendelev et al. (2019) v001 |
| EAM_Dynamo_MendelevUnderwoodAckland_2016pot1_Ti__MO_143373446649_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ti (parameter set 1) developed by Mendelev, Underwood, and Ackland (2016) v001 |
| EAM_Dynamo_MendelevUnderwoodAckland_2016pot3_Ti__MO_819959112190_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ti (parameter set 3) developed by Mendelev, Underwood, and Ackland (2016) v001 |
| EAM_Dynamo_MendelevUnderwoodAckland_2016pset2_Ti__MO_938747375043_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ti (parameter set 2) developed by Mendelev, Underwood, and Ackland (2016) v001 |
| EAM_Dynamo_Mishin_2004_NiAl__MO_101214310689_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Al system developed by Mishin (2004) v006 |
| EAM_Dynamo_MishinFarkasMehl_1999_Al__MO_651801486679_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Mishin et al. (1999) v006 |
| EAM_Dynamo_MishinFarkasMehl_1999_Ni__MO_400591584784_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Mishin et al. (1999) v006 |
| EAM_Dynamo_MishinMehlPapaconstantopoulos_2001_Cu__MO_346334655118_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Mishin, Mehl and Papaconstantopoulos (2001) v006 |
| EAM_Dynamo_MishinMehlPapaconstantopoulos_2002_NiAl__MO_109933561507_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the B2-NiAl compound developed by Mishin, Mehl, and Papaconstantopoulos (2002) v006 |
| EAM_Dynamo_NicholAckland_2016_Na__MO_048172193005_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Na developed by Nichol and Ackland (2016) v001 |
| EAM_Dynamo_NicholAckland_2016v2_Cs__MO_144828415103_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Cs developed by Nichol and Ackland (2016), version 2 refitted for better elastic constants v001 |
| EAM_Dynamo_NicholAckland_2016v2_Rb__MO_874930365376_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Rb developed by Nichol and Ackland (2016), version 2 refitted for better elastic constants v001 |
| EAM_Dynamo_NormanStarikovStegailov_2012_Au__MO_592431957881_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Norman, Starikov and Stegailov (2012) v001 |
| EAM_Dynamo_OBrienBarrPrice_2018_PtAu__MO_946831081299_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Pt-Au system developed by O'Brien et al. (2018) v001 |
| EAM_Dynamo_Olsson_2009_Fe__MO_024705128470_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Olsson (2009) v001 |
| EAM_Dynamo_Olsson_2009_V__MO_944449444863_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for V developed by Olsson (2009) v001 |
| EAM_Dynamo_Olsson_2009_W__MO_670013535154_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for W developed by Olsson (2009) v001 |
| EAM_Dynamo_Olsson_2010_Au__MO_228280943430_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Olsson (2010) v001 |
| EAM_Dynamo_OnatDurukanoglu_2014_CuNi__MO_592013496703_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu-Ni alloys developed by Onat and Durukanoğlu (2014) v006 |
| EAM_Dynamo_PanBorovikovMendelev_2108_AgNi__MO_222110751402_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ag-Ni system developed by Pan et al. (2018) v001 |
| EAM_Dynamo_Pun_2017_Au__MO_188701096956_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Pun (2017) v001 |
| EAM_Dynamo_PunMishin_2009_NiAl__MO_751354403791_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Al system developed by Purja Pun and Minshin (2009) v006 |
| EAM_Dynamo_PunMishin_2012_Co__MO_885079680379_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for hcp and fcc Cobalt developed by Purja Pun and Mishin (2012) v006 |
| EAM_Dynamo_PunYamakovMishin_2013_AlCo__MO_678952612413_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Al-Co system developed by Pun, Yamakov and Mishin (2013) v001 |
| EAM_Dynamo_PunYamakovMishin_2013_NiAlCo__MO_826591359508_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Al-Co system developed by Pun, Yamakov and Mishin (2013) v001 |
| EAM_Dynamo_PunYamakovMishin_2015_NiCo__MO_010613863288_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Co system developed by Pun, Yamakov and Mishin (2015) v001 |
| EAM_Dynamo_RaveloGermannGuerrero_2013Ta1_Ta__MO_816821594689_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for developed by Ravelo et al. (2013); Ta1 Interaction v001 |
| EAM_Dynamo_RaveloGermannGuerrero_2013Ta2_Ta__MO_330376344314_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ta developed by Ravelo et al. (2013); Ta2 interaction v001 |
| EAM_Dynamo_SamolyukBelandStocks_2016_NiPd__MO_532072268679_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Pd system developed by Samolyuk et al. (2016) v001 |
| EAM_Dynamo_SchopfBrommerFrigan_2012_AlMnPd__MO_137572817842_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Al-Mn-Pd system developed by Schopf et al. (2012) v001 |
| EAM_Dynamo_SetyawanGaoKurtz_2018_ReW__MO_680820064987_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the W-Re system developed by Setyawan, Gao, and Kurtz (2018) v001 |
| EAM_Dynamo_ShengKramerCadien_2011_Ca__MO_382426954533_001 | eam | EAM potential for Ca developed by Sheng et al. (2011) v001 |
| EAM_Dynamo_SmirnovaKuskinStarikov_2013_UMoXe__MO_679329885632_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the ternary U-Mo-Xe system developed by Smirnova et al. (2013) v006 |
| EAM_Dynamo_SmirnovaStarikovStegailov_2012_U__MO_649864794085_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for U developed by Smirnova, Starikov, and Stegailov (2012) v001 |
| EAM_Dynamo_SongMendelev_2021_AlSm__MO_722733117926_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Al-Sm system developed by Song and Mendelev (2021) v001 |
| EAM_Dynamo_StollerTammBeland_2016_Ni__MO_103383163946_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Stoller et al. (2016) v001 |
| EAM_Dynamo_SturgeonLaird_2000_Al__MO_120808805541_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al optimized for melting temperature developed by Sturgeon and Laird (2000) v006 |
| EAM_Dynamo_SunMendelevBecker_2006_Mg__MO_848345414202_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Mg developed by Sun et al. (2006) v006 |
| EAM_Dynamo_SunZhangMendelev_2022_Fe__MO_044341472608_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Sun et al. (2022) v001 |
| EAM_Dynamo_TehranchiCurtin_2010_NiH__MO_535504325462_004 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni-H with enhanced binding of H atoms to Ni grain boundaries by Tehranchi and Curtin (2017) v004 |
| EAM_Dynamo_VailheFarkas_1997_CoAl__MO_284963179498_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Co-Al system developed by Vailhé and Farkas (1997) v006 |
| EAM_Dynamo_VoterChen_1993_Ag__MO_504158228467_001 | eam | EAM potential for Ag developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Al__MO_986439208975_001 | eam | EAM potential for Al developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Au__MO_355170963718_001 | eam | EAM potential for Au developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Cu__MO_808763161866_001 | eam | EAM potential for Cu developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Ni__MO_071073566434_001 | eam | EAM potential for Ni developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Pd__MO_150533546179_001 | eam | EAM potential for Pd developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Pt__MO_101785259970_001 | eam | EAM potential for Pt developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_WangZhuXiang_2018pot2_Pb__MO_961101070310_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pb (parameter set 2) developed by Wang et al. (2018) v001 |
| EAM_Dynamo_Wen_2021_FeH__MO_634187028437_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Fe-H system developed by Wen (2021) v001 |
| EAM_Dynamo_WilliamsMishinHamilton_2006_Ag__MO_131620013077_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ag developed by Williams, Mishin and Hamilton (2006) v006 |
| EAM_Dynamo_WilliamsMishinHamilton_2006_CuAg__MO_128703483589_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Cu-Ag system developed by Williams et al. (2006) v006 |
| EAM_Dynamo_WilsonMendelev_2015_NiZr__MO_306032198193_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the Ni-Zr system developed by Wilson and Mendelev (2015) v001 |
| EAM_Dynamo_WilsonMendelev_2016_Mg__MO_574574915905_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Mg developed by Wilson and Mendelev (2016) v001 |
| EAM_Dynamo_WineyKubotaGupta_2010_Al__MO_149316865608_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al for shock compression at room and higher temperatures developed by Winey, Kubota and Gupta (2010) v006 |
| EAM_Dynamo_WuTrinkle_2009_CuAg__MO_270337113239_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Cu-Ag system developed by Wu and Trinkle (2009) v006 |
| EAM_Dynamo_Zhakhovsky_2009_Al__MO_519613893196_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zhakhovsky et al. (2009) v001 |
| EAM_Dynamo_Zhakhovsky_2009_Au__MO_173248269481_002 | eam | EAM potential for gold in a wide range of compressions and temperatures v002 |
| EAM_Dynamo_ZhangAshcraftMendelev_2016_NiNb__MO_047308317761_001 | eam | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for the Ni-Nb system developed by Zhang et al. (2016) v001 |
| EAM_Dynamo_ZhouBarteltSills_2021_PdHHe__MO_865505436319_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Pd-H-He system developed by Zhou, Bartelt, and Sills (2021) v001 |
| EAM_Dynamo_ZhouFosterSills_2018_FeNiCr__MO_036303866285_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe-Ni-Cr developed by Zhou, Foster and Sills (2018) v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Ag__MO_947112899505_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ag developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Al__MO_131650261510_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Au__MO_468407568810_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Co__MO_924630542818_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Co developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Cu__MO_127245782811_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_CuAgAuNiPdPtAlPbFeMoTaWMgCoTiZr__MO_870117231765_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Cu-Ag-Au-Ni-Pd-Pt-Al-Pb-Fe-Mo-Ta-W-Mg-Co-Ti-Zr system developed by Zhou, Johnson, and Wadley (2004) v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_CuTa__MO_547744193826_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Cu-Ta system developed by Zhou, Johnson, and Wadley (2004) v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Fe__MO_650279905230_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Mg__MO_137404467969_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Mg developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Mo__MO_271256517527_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Mo developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Ni__MO_110256178378_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Pb__MO_116920074573_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pb developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Ta__MO_130046220009_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ta developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Ti__MO_723456820410_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pb developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_W__MO_524392058194_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for W developed by Zhou, Johnson and Wadley (2004) v005 v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Zr__MO_103270551167_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Zr developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Ag__MO_505250810900_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ag developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Al__MO_060567868558_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Au__MO_684444719999_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Co__MO_247800397145_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Co developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Cu__MO_759493141826_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_CuAgAu__MO_318213562153_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Cu-Ag-Au system developed by Zhou, Johnson and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_CuTa__MO_950828638160_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Cu-Ta system developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Fe__MO_681088298208_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Mg__MO_894868634445_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Mg developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Mo__MO_230319944007_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Mo developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Ni__MO_593762436933_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Pb__MO_988703794028_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pb developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Pd__MO_993644691224_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pd developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Pt__MO_601539325066_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pt developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Ta__MO_568033730744_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ta developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Ti__MO_101966451181_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Ti developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_W__MO_914556822329_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for W developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Zr__MO_380166217430_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Zr developed by Zhou, Johnson, and Wadley (2004); NIST retabulation v001 |
| EAM_Dynamo_ZhouWadleyJohnson_2001_Al__MO_049243498555_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zhou, Wadley and Johnson (2001) v001 |
| EAM_Dynamo_ZhouWadleyJohnson_2001_Cu__MO_380822813353_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Zhou, Wadley and Johnson (2001) v001 |
| EAM_Dynamo_ZhouWadleyJohnson_2001_Pt__MO_102190350384_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Pt developed by Zhou, Wadley and Johnson (2001) v006 |
| EAM_Dynamo_ZhouWadleyJohnson_2001_W__MO_621445647666_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for W developed by Zhou, Wadley and Johnson (2001) v001 |
| EAM_Dynamo_ZhouZimmermanWong_2008_PdH__MO_114797992931_001 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Pd-H system developed by Zhou et al. (2008) v001 |
| EAM_Dynamo_ZopeMishin_2003_Al__MO_664470114311_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zope and Mishin (2003) v006 |
| EAM_Dynamo_ZopeMishin_2003_TiAl__MO_117656786760_006 | eam | EAM potential (LAMMPS cubic hermite tabulation) for the Ti-Al system developed by Zope and Mishin (2003) v006 |
| EAM_ErcolessiAdams_1994_Al__MO_324507536345_003 | eam | Glue potential (EAM-style) (LAMMPS cubic hermite tabulation) for Al developed by Ercolessi and Adams (1994) v003 |
| EAM_IMD_BrommerBoissieuEuchner_2009_MgZn__MO_710767216198_003 | eam | EAM potential (IMD tabulation) for the Mg-Zn system developed by Brommer et al. (2009) v003 |
| EAM_IMD_BrommerGaehler_2006A_AlNiCo__MO_122703700223_003 | eam | EAM potential (IMD tabulation) for the Al-Ni-Co system for quasicrystals developed by Brommer and Gaehler (2006); Potential A v003 |
| EAM_IMD_BrommerGaehler_2006B_AlNiCo__MO_128037485276_003 | eam | EAM potential (IMD tabulation) for the Al-Ni-Co system for quasicrystals developed by Brommer and Gaehler (2006); Potential B v003 |
| EAM_IMD_BrommerGaehlerMihalkovic_2007_CaCd__MO_145183423516_003 | eam | EAM potential (IMD tabulation) for the Ca-Cd system developed by Brommer, Gaehler and Mihalkovic (2007) v003 |
| EAM_IMD_SchopfBrommerFrigan_2012_AlMnPd__MO_878712978062_003 | eam | EAM potential (IMD tabulation) for the Al-Mn-Pd system developed by Schopf et al. (2012) v003 |
| EAM_Magnetic2GQuintic_ChiesaDerletDudarev_2011_Fe__MO_140444321607_002 | eam | EAM potential (2nd gen magnetic, quintic tabulation) for magnetic Fe developed by Chiesa et al. (2011) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_Mo__MO_424746498193_002 | eam | EAM potential (magnetic, cubic tabulation) for Mo developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_Nb__MO_218026715338_002 | eam | EAM potential (magnetic, cubic tabulation) for Nb developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_Ta__MO_261274272789_002 | eam | EAM potential (magnetic, cubic tabulation) for Ta developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_V__MO_683890323730_002 | eam | EAM potential (magnetic, cubic tabulation) for V developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_W__MO_195478838873_002 | eam | EAM potential (magnetic, cubic tabulation) for W developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DudarevDerlet_2005_Fe__MO_135034229282_002 | eam | EAM potential (magnetic, cubic tabulation) for magnetic Fe developed by Dudarev and Derlet (2005) v002 |
| EAM_MagneticCubic_MendelevHanSrolovitz_2003_Fe__MO_856295952425_002 | eam | EAM potential (cubic tabulation) for Fe developed by Mendelev et al. (2003) v002 |
| EAM_Mendelev_2019_CuZr__MO_945018740343_000 | eam | EAM potential for Cu-Zr developed by Mendelev (2019) v000 |
| EAM_NN_Johnson_1988_Cu__MO_887933271505_003 | eam | EAM Potential (analytical nearest-neighbor) for Cu developed by Johnson (1988) v003 |
| EAM_QuinticClampedSpline_ErcolessiAdams_1994_Al__MO_450093727396_003 | eam | EAM potential (clamped quintic tabulation) for Al developed by Ercolessi and Adams (1994) v003 |
| EAM_QuinticClampedSpline_Kim_2021_PtAu__MO_463728687265_001 | eam | EAM potential (clamed quintic spline) for the Pt-Au system developed by Kim (2021) based on Brien et al. (2018) v001 |
| EAM_QuinticHermiteSpline_ErcolessiAdams_1994_Al__MO_781138671863_003 | eam | EAM potential (quintic hermite tabulation) for Al developed by Ercolessi and Adams (1994) v003 |
| EMT_Asap_MetalGlass_BaileySchiotzJacobsen_2004_CuMg__MO_228059236215_001 | eam | EMT potential for Cu-Mg metallic glasses developed by Bailey, Schiotz, and Jacobsen (2004) v001 |
| EMT_Asap_MetalGlass_CuMgZr__MO_655725647552_002 | eam | Effective Medium Theory potential for CuMg and CuZr alloys, in particular metallic glasses. |
| EMT_Asap_MetalGlass_PaduraruKenoufiBailey_2007_CuZr__MO_987541074959_001 | eam | EMT potential for Cu-Zr metallic glasses developed by Paduraru et al. (2007) v001 |
| EMT_Asap_Standard_Jacobsen_Stoltze_Norskov_AlAgAuCuNiPdPt__MO_118428466217_002 | eam | Standard Effective Medium Theory potential for face-centered cubic metals as implemented in ASE/Asap. |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Ag__MO_303974873468_001 | eam | EMT potential for Ag developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Al__MO_623376124862_001 | eam | EMT potential for Al developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_AlAgAuCuNiPdPt__MO_115316750986_001 | eam | EMT potential for Al, Ni, Cu, Pd, Ag, Pt and Au developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Au__MO_017524376569_001 | eam | EMT potential for Au developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Cu__MO_396616545191_001 | eam | EMT potential for Cu developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Ni__MO_108408461881_001 | eam | EMT potential for Ni developed by Jacobsen, Stolze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Pd__MO_066802556726_001 | eam | EMT potential for Pd developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Pt__MO_637493005914_001 | eam | EMT potential for Pt developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| Sim_ASAP_EMT_Rasmussen_AgAuCu__SM_847706399649_000 | eam | ASAP EMT potential optimized for experimental stacking fault energies developed by Rasmussen v000 |
| Sim_LAMMPS_EAM_BonnyBakaevTerentyev_2022_FeCCr__SM_747012431642_000 | eam | LAMMPS EAM Potential developed by Bonny, Bakaev, and Terentyev to study radiation-induced defects in Cr steel (2022) v000 |
| Sim_LAMMPS_EAM_BonnyCastinBullens_2013_FeCrW__SM_699257350704_001 | eam | LAMMPS EAM potential for Fe-Cr-W developed by Bonny et al. (2013) v001 |
| Sim_LAMMPS_EAM_BonnyPasianotTerentyev_2011_FeCr__SM_237089298463_001 | eam | LAMMPS EAM potential for Fe-Cr developed by Bonny et al. (2011) v001 |
| Sim_LAMMPS_EAM_EichBeinkeSchmitz_2015_FeCr__SM_731771351835_000 | eam | EAM/TBM potential for Fe–Cr developed by Eich et al. (2015) v000 |
| Sim_LAMMPS_EAMCD_StukowskiSadighErhart_2009_FeCr__SM_775564499513_000 | eam | LAMMPS Concentration-Dependent EAM potential for Fe-Cr developed by Stukowski et al. (2009) v000 |
| Model | Type | Title |
|---|---|---|
| EDIP_BelkoGusakovDorozhkin_2010_Ge__MO_129433059219_001 | edip | EDIP model for Ge developed by Belko, Gusakov and Dorozhkin (2010) v001 |
| EDIP_JustoBazantKaxiras_1998_Si__MO_958932894036_002 | edip | EDIP model for Si developed by Justo et al. (1998) v002 |
| EDIP_LAMMPS_JiangMorganSzlufarska_2012_SiC__MO_667792548433_000 | edip | EDIP model for SiC developed by Jiang, Morgan, and Szlufarska (2012) v000 |
| EDIP_LAMMPS_JustoBazantKaxiras_1998_Si__MO_315965276297_000 | edip | EDIP model for Si developed by Justo et al. (1998) v000 |
| EDIP_LAMMPS_LucasBertolusPizzagalli_2009_SiC__MO_634310164305_000 | edip | EDIP potential for Si-C developed by Lucas, Bertolus, and Pizzagalli (2009) v000 |
| EDIP_LAMMPS_Marks_2000_C__MO_374144505645_000 | edip | EDIP potential for C developed by Marks (2000) v000 |
| Sim_LAMMPS_EDIP_LucasBertolusPizzagalli_2009_SiC__SM_435704953434_000 | edip | LAMMPS EDIP potential for Si-C developed by Lucas, Bertolus, and Pizzagalli (2009) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_EIM_Zhou_2010_BrClCsFIKLiNaRb__SM_259779394709_001 | eim | LAMMPS EIM potential for the Br-Cl-Cs-F-I-K-Li-Na-Rb system developed by Zhou (2010) v001 |
| Model | Type | Title |
|---|---|---|
| Exp6_KongChakrabarty_1973_ArNe__MO_946046425752_002 | exp6 | Exp-6 pair potential for Ar-Ne with parameters due to Hogervorst and mixing rule due to Kong and Chakrabarty (1973) v002 |
| Model | Type | Title |
|---|---|---|
| QUIP_GAP_SivaramanGallingtonKrishnamoorthy_2021_HfO__MO_200178964232_000 | gap | GAP model for Hf and O developed by Sivaraman, Gallington, and Krishnamoorthy et al. (2021) v000 |
| QUIP_GAP_SivaramanGuoWard_2021_LiCl__MO_225395104084_000 | gap | QUIP GAP potential developed by Sivaraman et al. for modeling molten LiCl salt (2021) v000 |
| QUIP_GAP_Xu_2003_Pt__MO_370837021112_000 | gap | GAP model for Pt developed by Xu (2023) v000 |
| Model | Type | Title |
|---|---|---|
| GEAM_LAMMPS_Samanta_2025_AlMgY__MO_534783049221_000 | geam | GEAM potential for the Al-Mg-Y system developed by Samanta (2025) v000 |
| GEAM_LAMMPS_Samanta_2025_MgY__MO_501769325268_000 | geam | GEAM potential for the Mg-Y system developed by Samanta (2025) v000 |
| GEAM_LAMMPS_ShiIyerSharma_2023_MoTaW__MO_785657481359_000 | geam | GEAM potential for the Mo-Ta-W system developed by Shi et al. (2023) v000 |
| GEAM_LAMMPS_ShiSamanta_2023_CrMoNbV__MO_397830378062_000 | geam | GEAM potential for the Cr-Mo-Nb-V system developed by Shi and Samanta (2023) v000 |
| GEAM_LAMMPS_ShiSamanta_2024_MoV__MO_337827773876_000 | geam | GEAM potential for the Mo-V system developed by Shi and Samanta (2024) v000 |
| GEAM_LAMMPS_WangSamanta_2026_LiTa__MO_118866552697_000 | geam | GEAM potential for the Li-Ta system developed by Wang and Samanta (2026) v000 |
| GEAM_LAMMPS_ZhaoSamanta_2024_FeNi__MO_361455675661_000 | geam | GEAM potential for the Fe-Ni system developed by Zhao and Samanta (2024) v000 |
| Model | Type | Title |
|---|---|---|
| ThreeBodyCluster_Gong_Gong_1993_Si__MO_407755720412_000 | gong | Three-body cluster potential for Si by Gong (1993) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_GW_GaoWeber_2002_SiC__SM_606253546840_000 | gw | LAMMPS Gao-Weber potential for Si-C developed by Gao and Weber (2002) v000 |
| Sim_LAMMPS_GWZBL_Samolyuk_2016_SiC__SM_720598599889_000 | gw | LAMMPS Gao-Weber potential combined with a modified repulsive ZBL core function for the Si-C system developed by German Samolyuk (2016) v000 |
| Model | Type | Title |
|---|---|---|
| hNN_WenTadmor_2019Grx_C__MO_421038499185_001 | hnn | A hybrid neural network potential for multilayer graphene systems developed by Wen and Tadmor (2019) v001 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_Hybrid_DuanXieGuo_2019_TaHe__SM_016305073020_001 | hybrid | LAMMPS hybrid table and EAM potential for the Ta-He system developed by Duan et al. (2019) v001 |
| Sim_LAMMPS_HybridOverlay_BelandLuOsetskiy_2016_CoNi__SM_445377835613_001 | hybrid | LAMMPS hybrid overlay EAM and ZBL potential for the Ni-Co system developed by Beland et al. (2016) v001 |
| Model | Type | Title |
|---|---|---|
| ThreeBodyBondOrder_KDS_KhorDasSarma_1988_C__MO_454320668790_000 | kds | Three-body cluster potential for C by Khor and Das Sarma (1988) v000 |
| ThreeBodyBondOrder_KDS_KhorDasSarma_1988_Ge__MO_216597146527_000 | kds | Three-body cluster potential for Ge by Khor and Das Sarma (1988) v000 |
| ThreeBodyBondOrder_KDS_KhorDasSarma_1988_Si__MO_722489435928_000 | kds | Three-body cluster potential for Si by Khor and Das Sarma (1988) v000 |
| Model | Type | Title |
|---|---|---|
| ThreeBodyCluster_KP_KaxirasPandey_1988_Si__MO_072486242437_000 | kp | Three-body cluster potential for Si by Kaxiras and Pandey (1988) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_LCBOP_LosFasolino_2003_C__SM_469631949122_000 | lcbop | LAMMPS LCBOP potential for C developed by Los and Fasolino (2003) v000 |
| Model | Type | Title |
|---|---|---|
| LJ_ElliottAkerson_2015_Universal__MO_959249795837_003 | lj | Efficient 'universal' shifted Lennard-Jones model for all KIM API supported species developed by Elliott and Akerson (2015) v003 |
| LJ_Shifted_Bernardes_1958HighCutoff_Ar__MO_242741380554_004 | lj | Lennard-Jones model (shifted) for Ar with parameters from Bernardes (1958) (high precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958HighCutoff_Kr__MO_923895531627_004 | lj | Lennard-Jones model (shfited) for Kr with parameters from Bernardes (1958) (high precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958HighCutoff_Ne__MO_966254629593_004 | lj | Lennard-Jones model (shifted) for Ne with parameters from Bernardes (1958) (high precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958HighCutoff_Xe__MO_796748253903_004 | lj | Lennard-Jones model (shifted) for Xe with parameters from Bernardes (1958) (high precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958LowCutoff_Ar__MO_720819638419_004 | lj | Lennard-Jones model (shifted) for Ar with parameters from Bernardes (1958) (low precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958LowCutoff_Kr__MO_995724792024_004 | lj | Lennard-Jones model (shifted) for Kr with parameters from Bernardes (1958) (low precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958LowCutoff_Ne__MO_466741694288_004 | lj | Lennard-Jones model (shifted) for Ne with parameters from Bernardes (1958) (low precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958LowCutoff_Xe__MO_648694198005_004 | lj | Lennard-Jones model (shifted) for Xe with parameters from Bernardes (1958) (low precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958MedCutoff_Ar__MO_126566794224_004 | lj | Lennard-Jones model (shifted) for Ar with parameters from Bernardes (1958) (medium precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958MedCutoff_Kr__MO_984281096460_004 | lj | Lennard-Jones model (shifted) for Kr with parameters from Bernardes (1958) (medium precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958MedCutoff_Ne__MO_160637895352_004 | lj | Lennard-Jones model (shifted) for Ne with parameters from Bernardes (1958) (medium precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958MedCutoff_Xe__MO_849320763277_004 | lj | Lennard-Jones model (shifted) for Xe with parameters from Bernardes (1958) (medium precision cutoff) v004 |
| LJ_Smoothed_Bernardes_1958_Ar__MO_764178710049_001 | lj | Lennard-Jones potential (smoothed) for Ar with parameters from Bernardes (1958) v001 |
| LJ_Truncated_Nguyen_2005_Ar__MO_398194508715_001 | lj | Lennard-Jones potential (truncated) for Ar with parameters from Nguyen (2005) v001 |
| Model | Type | Title |
|---|---|---|
| TorchML_MACE_BatatiaBennerChiang_2023_MP0a_medium__MO_568776921807_000 | mace | MACE MP 0 'medium' foundation model for atomistic materials chemistry v000 |
| TorchML_MACE_GuptaTadmorMartiniani_2024_Si__MO_781946209112_001 | mace | Parallel MACE Equivariant GNN for Si developed by Gupta et al. (2024) v001 |
| Model | Type | Title |
|---|---|---|
| MEAM_2NN_Fe_to_Ga__MO_145522277939_001 | meam | Model parameterization of 2NN MEAM model |
| MEAM_2NN_GaInN__MO_117938381510_001 | meam | Model parameterization of 2NN MEAM model |
| MEAM_2NN_LiSi__MO_596436139350_001 | meam | meam potential for Li-Si alloys |
| MEAM_LAMMPS_AgrawalMirzaeifar_2021_CuC__MO_028979335952_002 | meam | MEAM potential for Cu-C composites developed by Agrawal and Mirzaeifar (2021) v002 |
| MEAM_LAMMPS_AhmadGrohGhazisaeidi_2018_MgY__MO_135739722270_002 | meam | MEAM potential for Mg–Y alloys developed by Ahmad et al. (2018) v002 |
| MEAM_LAMMPS_AlmyrasSangiovanniSarakinos_2019_NAlTi__MO_958395190627_002 | meam | MEAM potential for the N-Al-Ti system developed by Almyras et al. v002 |
| MEAM_LAMMPS_AlviFaiyadMunshi_2022_AgAu__MO_511467482222_000 | meam | MEAM potential developed by Alvi et al. for cyclic loading of Ag–Au composite nanowire (2022) v000 |
| MEAM_LAMMPS_AsadiZaeemNouranian_2015_Cu__MO_390178379548_002 | meam | MEAM potential for Cu developed by Asadi et al. (2015) v002 |
| MEAM_LAMMPS_AsadiZaeemNouranian_2015_Fe__MO_492310898779_002 | meam | MEAM potential for Fe developed by Asadi et al. (2015) v002 |
| MEAM_LAMMPS_AsadiZaeemNouranian_2015_Ni__MO_700541006254_002 | meam | MEAM potential for Ni developed by Asadi et al. (2015) v002 |
| MEAM_LAMMPS_AslamBaskesDickel_2019_FeMnSiC__MO_427873955970_002 | meam | MEAM Potential for the Fe-Mn-Si-C system developed by Aslam et al. (2019) v002 |
| MEAM_LAMMPS_ChoiJoSohn_2018_CoNiCrFeMn__MO_115454747503_002 | meam | MEAM Potential for the Co-Ni-Cr-Fe-Mn system developed by Choi et al., (2018) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_CoCr__MO_410167849923_002 | meam | MEAM Potential for the Co-Cr system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_CoFe__MO_179158257180_002 | meam | MEAM Potential for the Co-Fe system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_CoMn__MO_808662295149_002 | meam | MEAM Potential for the Co-Mn system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_CrMn__MO_671124822359_002 | meam | MEAM Potential for the Cr-Mn system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_NiMn__MO_348689608050_002 | meam | MEAM Potential for the Ni-Mn system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_CostaAgrenClavaguera_2007_AlNi__MO_131642768288_002 | meam | MEAM Potential for the Al-Ni system developed by Silva et al. (2007) v002 |
| MEAM_LAMMPS_CuiGaoCui_2012_LiSi__MO_557492625287_002 | meam | MEAM potential for Li-Si alloys developed by Cui et al. (2012) v002 |
| MEAM_LAMMPS_DickelBaskesAslam_2018_MgAlZn__MO_093637366498_002 | meam | MEAM potential for Mg–Al–Zn alloys developed by Dickel et al. (2018) v002 |
| MEAM_LAMMPS_DongKimKo_2012_CoAl__MO_099716416216_002 | meam | MEAM Potential for the Co-Al system developed by Dong et al. (2012) v002 |
| MEAM_LAMMPS_DoShinLee_2008_In__MO_439532348190_001 | meam | MEAM Potential for In developed by Do, Shin and Lee (2008) v001 |
| MEAM_LAMMPS_DoShinLee_2009_GaInN__MO_815057898706_002 | meam | MEAM Potential for the Ga-In-N system developed by Do et al. (2009) v002 |
| MEAM_LAMMPS_DuLenoskyHennig_2011_Si__MO_883726743759_002 | meam | Spline-based MEAM potential for Si system developed by Du et al. (2011) v002 |
| MEAM_LAMMPS_EtesamiAsadi_2018_Cu__MO_227887284491_002 | meam | MEAM potential for Cu developed by Etesami and Asadi (2018) v002 |
| MEAM_LAMMPS_EtesamiAsadi_2018_Fe__MO_549900287421_002 | meam | MEAM potential for Fe developed by Etesami and Asadi (2018) v002 |
| MEAM_LAMMPS_EtesamiAsadi_2018_Ni__MO_937008984446_002 | meam | MEAM potential for Ni developed by Etesami and Asadi (2018) v002 |
| MEAM_LAMMPS_EtesamiBaskesLaradji_2018_PbSn__MO_162736908871_002 | meam | MEAM potential for Pb-Sn developed by Etesami et al. (2018) v002 |
| MEAM_LAMMPS_FernandezPascuet_2014_U__MO_399431830125_002 | meam | MEAM potential for U developed by Fernández and Pascuet (2014) v002 |
| MEAM_LAMMPS_FuemmelerVita_2023_Li__MO_386038428339_000 | meam | MEAM spline potential for Li developed by Fuemmeler and Vita (2023) v000 |
| MEAM_LAMMPS_GaoOteroAouadi_2013_AgTaO__MO_112077942578_002 | meam | MEAM potential for perovskite silver tantalate (AgTaO3) developed by Gao et al. (2013) v002 |
| MEAM_LAMMPS_HennigLenoskyTrinkle_2008_Ti__MO_520569947398_002 | meam | MEAM potential for Ti developed by Hennig et al. (2008) v002 |
| MEAM_LAMMPS_HiremathMelinBitzek_2022_W__MO_943864507178_001 | meam | MEAM Potential for W developed by Hiremath et al. (2022) v001 |
| MEAM_LAMMPS_HuangDongLiu_2018_Si__MO_050147023220_002 | meam | MEAM potential for Si developed by Huang et al. (2018) v002 |
| MEAM_LAMMPS_HuangLiuDuan_2021_HfNbTaTiZr__MO_893505888031_002 | meam | MEAM potential for HfNbTaTiZr alloy developed by Huang et al. (2021) v002 |
| MEAM_LAMMPS_JangKimLee_2018_ZnMg__MO_474962707676_002 | meam | MEAM Potential for the Mg-Zn system developed by Jang et al. (2018) v002 |
| MEAM_LAMMPS_JangSeolLee_2019_CaZnMg__MO_708495328010_002 | meam | MEAM Potential for the Ca-Zn-Mg system developed by Jang, Seol and Lee (2019) v002 |
| MEAM_LAMMPS_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__MO_262519520678_002 | meam | MEAM potential for Al-Si-Mg-Cu-Fe alloys developed by Jelinek et al. (2012) v002 |
| MEAM_LAMMPS_JeongLee_2020_PdC__MO_068985622065_002 | meam | MEAM Potential for the Pd-C system developed by Jeong, and Lee (2020) v002 |
| MEAM_LAMMPS_JeongLee_2020_PtC__MO_716623333967_002 | meam | MEAM Potential for the Pt-C system developed by Jeong, and Lee (2020) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdAl__MO_616482358807_002 | meam | MEAM Potential for the Pd-Al system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdCo__MO_101997554790_002 | meam | MEAM Potential for the Pd-Co system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdCu__MO_353393547686_002 | meam | MEAM Potential for the Pd-Cu system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdFe__MO_924736622203_002 | meam | MEAM Potential for the Pd-Fe system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdMo__MO_356501945107_002 | meam | MEAM Potential for the Pd-Mo system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdNi__MO_008996216289_002 | meam | MEAM Potential for the Pd-Ni system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdTi__MO_086900950763_002 | meam | MEAM Potential for the Pd-Ti system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_KangEunJun_2014_SiC__MO_477506997611_002 | meam | MEAM Potential for the Si-C system developed by Kang et al. (2014) v002 |
| MEAM_LAMMPS_KangSaLee_2009_ZrAgCu__MO_813575892799_002 | meam | MEAM Potential for the Zr-Ag-Cu system developed by Kang et al. (2009) v002 |
| MEAM_LAMMPS_KavousiNovakBaskes_2019_NiTi__MO_050461957184_002 | meam | MEAM potential for Ni-Ti alloys developed by Kavousi et al, (2019) v002 |
| MEAM_LAMMPS_KimJeonLee_2015_MgCa__MO_611309973581_002 | meam | MEAM Potential for the Mg-Ca system developed by Kim, Jeon and Lee (2015) v002 |
| MEAM_LAMMPS_KimJeonLee_2015_MgSn__MO_935641703527_002 | meam | MEAM Potential for the Mg-Sn system developed by Kim, Jeon, and Lee (2015) v002 |
| MEAM_LAMMPS_KimJeonLee_2015_MgY__MO_018428823000_002 | meam | MEAM Potential for the Mg-Y system developed by Kim, Jeon, and Lee (2015) v002 |
| MEAM_LAMMPS_KimJungLee_2009_FeTiC__MO_110119204723_002 | meam | MEAM Potential for the Fe-Ti-C system developed by Kim, Jung, Lee (2009) v002 |
| MEAM_LAMMPS_KimJungLee_2010_FeNbC__MO_072689718616_002 | meam | MEAM Potential for the Fe-Nb-C system developed by Kim and Lee (2010) v002 |
| MEAM_LAMMPS_KimJungLee_2012_LiMg__MO_427397414195_002 | meam | MEAM Potential for the Li-Mg system developed by Kim, Jung, and Lee (2012) v002 |
| MEAM_LAMMPS_KimJungLee_2015_NiAlCo__MO_876687166519_002 | meam | MEAM Potential for the Ni-Al-Co system developed by Kim, Jung, and Lee, (2015) v002 |
| MEAM_LAMMPS_KimKimJung_2016_AlTi__MO_618133763375_002 | meam | MEAM Potential for the Al-Ti system developed by Kim et al. (2016) v002 |
| MEAM_LAMMPS_KimKimJung_2017_NiAlTi__MO_478967255435_002 | meam | MEAM Potential for the Ni-Al-Ti system developed by Kim et al. (2017) v002 |
| MEAM_LAMMPS_KimKimLee_2009_AlMg__MO_058537087384_002 | meam | MEAM Potential for the Al-Mg system developed by Kim, Kim, and Lee (2009) v002 |
| MEAM_LAMMPS_KimKoLee_2020_Na__MO_321355778754_002 | meam | MEAM Potential for the pure Na developed by Kim, Ko and Lee (2020) v002 |
| MEAM_LAMMPS_KimKoLee_2020_NaSn__MO_329881861557_002 | meam | MEAM Potential for the Na-Sn system developed by Kim, Ko and Shim (2020) v002 |
| MEAM_LAMMPS_KimLee_2006_PtFe__MO_343168101490_002 | meam | MEAM Potential for the Pt-Fe system developed by Kim, Koo, and Lee (2006) v002 |
| MEAM_LAMMPS_KimLee_2008_CuZr__MO_407917731909_001 | meam | MEAM Potential for the Cu-Zr system developed by Kim and Lee (2008) v001 |
| MEAM_LAMMPS_KimLee_2008_TiC__MO_134206624109_002 | meam | MEAM Potential for the Ti-C system developed by Kim, and Lee (2008) v002 |
| MEAM_LAMMPS_KimLee_2008_TiN__MO_070542625990_002 | meam | MEAM Potential for the Ti-N system developed by Kim and Lee (2008) v002 |
| MEAM_LAMMPS_KimLee_2017_MgNd__MO_059320827436_002 | meam | MEAM Potential for the Mg-Nd system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimLee_2017_MgPb__MO_325675357262_002 | meam | MEAM Potential for the Mg-Pb system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimLeeBaskes_2006_Ti__MO_472654156677_001 | meam | MEAM Potential for Ti developed by Kim, Lee, and Baskes (2006) v001 |
| MEAM_LAMMPS_KimLeeBaskes_2006_Zr__MO_392493010449_001 | meam | MEAM Potential for Zr developed by Kim, Lee, and Baskes (2006) v001 |
| MEAM_LAMMPS_KimSeolJi_2017_PtAl__MO_793141037706_002 | meam | MEAM Potential for the Pt-Al system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtCo__MO_545073984441_002 | meam | MEAM Potential for the Pt-Co system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtCu__MO_070797404269_002 | meam | MEAM Potential for the Pt-Cu system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtMo__MO_831380044253_002 | meam | MEAM Potential for the Pt-Mo system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_002 | meam | MEAM Potential for the Pt-Ni system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtTi__MO_280985530673_002 | meam | MEAM Potential for the Pt-Ti system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtV__MO_912978207512_002 | meam | MEAM Potential for the Pt-V system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimShinLee_2008_Ge__MO_657096500078_001 | meam | MEAM Potential for Ge developed by Kim, Shin and Lee (2008) v001 |
| MEAM_LAMMPS_KimShinLee_2009_FeMn__MO_058735400462_002 | meam | MEAM Potential for the Fe-Mn system developed by Kim, Shin, Lee (2009) v002 |
| MEAM_LAMMPS_KoGrabowskiNeugebauer_2015_NiTi__MO_663355627503_002 | meam | MEAM potential for Ni-Ti developed by Ko, Grabowski, and Neugebauer (2015) v002 |
| MEAM_LAMMPS_KoJimLee_2012_FeP__MO_179420363944_002 | meam | MEAM Potential for the Fe-P system developed by Ko, Kim, and Lee (2012) v002 |
| MEAM_LAMMPS_KoKimKwon_2018_Sn__MO_129364204512_002 | meam | MEAM potential for the pure tin (Sn) system developed by Ko et al. (2018) v002 |
| MEAM_LAMMPS_KoLee_2013_VPdY__MO_046547823135_002 | meam | MEAM Potential for the V-Pd-Y system developed by Ko and Lee (2013) v002 |
| MEAM_LAMMPS_KoShimLee_2011_AlH__MO_127847080751_002 | meam | MEAM Potential for the Al-H system developed by Ko, Shim, and Lee (2011) v002 |
| MEAM_LAMMPS_KoShimLee_2011_NiH__MO_091278480940_002 | meam | MEAM Potential for the Ni-H system developed by Ko et al. (2011) v002 |
| MEAM_LAMMPS_Lee_2006_FeC__MO_856956178669_002 | meam | MEAM Potential for the Fe-C system developed by Lee (2008) v002 |
| MEAM_LAMMPS_Lee_2007_Si__MO_774917820956_001 | meam | MEAM Potential for Si developed by Lee (2007) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Cr__MO_134550636109_001 | meam | MEAM Potential for Cr developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Fe__MO_196726067688_001 | meam | MEAM Potential for Fe developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Mo__MO_805823015127_001 | meam | MEAM Potential for Mo developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Nb__MO_802302521552_001 | meam | MEAM Potential for Nb developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Ta__MO_644143102837_001 | meam | MEAM Potential for Ta developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_V__MO_868364924829_001 | meam | MEAM Potential for V developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_W__MO_227263111062_001 | meam | MEAM Potential for W developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeJang_2007_FeH__MO_095610951957_002 | meam | MEAM Potential for the Fe-H system developed by Lee and Jang (2007) v002 |
| MEAM_LAMMPS_LeeLee_2005_C__MO_996970420049_001 | meam | MEAM Potential for C developed by Lee and Lee (2005) v001 |
| MEAM_LAMMPS_LeeLee_2010_FeAl__MO_332211522050_002 | meam | MEAM Potential for the Fe-Al system developed by Lee, and Lee. (2010) v002 |
| MEAM_LAMMPS_LeeLee_2014_ZrH__MO_946208788356_002 | meam | MEAM Potential for the Zr-H system developed by Lee and Lee (2014) v002 |
| MEAM_LAMMPS_LeeLeeKim_2006_FeN__MO_432861766738_002 | meam | MEAM Potential for the Fe-N system developed by Lee, Lee and Kim. (2006) v002 |
| MEAM_LAMMPS_LeeShim_2004_NiCu__MO_409065472403_002 | meam | MEAM Potential for the Ni-Cu system developed by Lee and Shim (2004) v002 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Ag__MO_969318541747_001 | meam | MEAM Potential for Ag developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Al__MO_353977746962_001 | meam | MEAM Potential for Al developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Au__MO_774911580446_001 | meam | MEAM Potential for Au developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Cu__MO_087820130586_001 | meam | MEAM Potential for Cu developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Ni__MO_000553624872_001 | meam | MEAM Potential for Ni developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Pb__MO_019208265157_001 | meam | MEAM Potential for Pb developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Pd__MO_307252285625_001 | meam | MEAM Potential for Pd developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Pt__MO_534993486058_001 | meam | MEAM Potential for Pt developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimPark_2001_FeCr__MO_150993986463_001 | meam | MEAM Potential for the Fe-Cr system developed by Lee, Shim and Park (2001) v001 |
| MEAM_LAMMPS_LeeWirthShim_2005_FeCu__MO_063626065437_002 | meam | MEAM Potential for the Fe-Cu system developed by Lee et al. (2005) v002 |
| MEAM_LAMMPS_Lenosky_2017_W__MO_999198119251_002 | meam | MEAM Potential for W developed by Lenosky (2017) v002 |
| MEAM_LAMMPS_LenoskySadighAlonso_2000_Si__MO_533426548156_002 | meam | MEAM potential for Si system developed by Lenosky et al. (2000) v002 |
| MEAM_LAMMPS_LiyanageKimHouze_2014_FeC__MO_075279800195_002 | meam | MEAM potential for Fe-C developed by Liyanage et al. (2014) v002 |
| MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlFe__MO_304347095149_001 | meam | MEAM Potential for the Al-Fe system developed by Mahata, Mukhopadhyay and Asle Zaeem (2022) v001 |
| MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi__MO_461927113651_001 | meam | MEAM Potential for the Al-Ni system developed by Mahata, Mukhopadhyay and Asle Zaeem (2022) v001 |
| MEAM_LAMMPS_MaiselKoZhang_2017_VNiTi__MO_744610363128_002 | meam | MEAM potential for V-Ni-Ti developed by Maisel et al. (2017) v002 |
| MEAM_LAMMPS_MirazDhariwalMeng_2020_CuNTi__MO_122936827583_002 | meam | MEAM potential for Ti/TiN and Cu/TiN interfaces developed by Miraz et al. (2020) v002 |
| MEAM_LAMMPS_MooreBeelerDeo_2015_UZr__MO_453094726678_001 | meam | MEAM potential for U-Zr alloy developed by Moore et al. (2015) v001 |
| MEAM_LAMMPS_MurallesParkKim_NiTi__MO_182729415169_000 | meam | MEAM potential for Ni-Ti developed by Muralles et al. (2017) v000 |
| MEAM_LAMMPS_NouranianTschoppGwaltney_2014_CH__MO_354152387712_002 | meam | MEAM potential for saturated hydrocarbons developed by Nouranian et al. (2014) v002 |
| MEAM_LAMMPS_OhSeolLee_2020_CoTi__MO_862371677648_002 | meam | MEAM Potential for the Co-Ti system developed by Oh, Seol, and Lee (2020) v002 |
| MEAM_LAMMPS_OhSeolLee_2020_CoV__MO_771146361182_002 | meam | MEAM Potential for the Co-V system developed by Oh, Seol, and Lee (2020) v002 |
| MEAM_LAMMPS_ParkFellingerLenosky_2012_Mo__MO_269937397263_002 | meam | MEAM Potential for Mo developed by Park et al. (2012) v002 |
| MEAM_LAMMPS_ParkFellingerLenosky_2012_Ta__MO_105449194206_002 | meam | MEAM Potential for Ta developed by Park et al. (2012) v002 |
| MEAM_LAMMPS_ParkFellingerLenosky_2012_W__MO_560940542741_002 | meam | MEAM Potential for W developed by Park et al. (2012) v002 |
| MEAM_LAMMPS_PascuetFernandez_2015_Al__MO_315820974149_002 | meam | MEAM potential for Al developed by Pascuet and Fernandez (2015) v002 |
| MEAM_LAMMPS_PascuetFernandez_2015_AlU__MO_596300673917_002 | meam | MEAM potential for Al-U developed by Pascuet and Fernandez (2015) v002 |
| MEAM_LAMMPS_RoyDuttaChakraborti_2021_AlLi__MO_971738391444_001 | meam | MEAM potential for Al and Al-Li alloys developed by Roy, Dutta, and Chakraborti (2021) v001 |
| MEAM_LAMMPS_SaLee_2008_FeTi__MO_260546967793_002 | meam | MEAM Potential for the Fe-Ti system developed by Sa and Lee (2008) v002 |
| MEAM_LAMMPS_SaLee_2008_NbFe__MO_162036141261_002 | meam | MEAM Potential for the Nb-Fe system developed by Sa and Lee (2008) v002 |
| MEAM_LAMMPS_SharifiWick_2025_FeMnNiTiCuCrCoAl__MO_675947402254_000 | meam | MEAM Potential for Fe, Mn, Ni, Ti, Cu, Cr, Co, and Al, developed by Sharifi and Wick (2025) v000 |
| MEAM_LAMMPS_ShimKoKim_2013_AlVH__MO_344724145339_002 | meam | MEAM Potential for the Al-V-H system developed by Shim et al. (2013) v002 |
| MEAM_LAMMPS_ShimKoKim_2013_NiVH__MO_612225165948_002 | meam | MEAM Potential for the Ni-V-H system developed by Shim et al. (2013) v002 |
| MEAM_LAMMPS_ShimLeeFleury_2011_VH__MO_072444764353_002 | meam | MEAM Potential for the V-H system developed by Shim et al. (2011) v002 |
| MEAM_LAMMPS_ShimParkCho_2003_NiW__MO_500937681860_002 | meam | MEAM Potential for the Ni-W system developed by Shim et al. (2003) v002 |
| MEAM_LAMMPS_SunRamachandranWick_2018_TiAl__MO_022920256108_002 | meam | MEAM potential for TiAl alloys developed by Sun et al. (2018) v002 |
| MEAM_LAMMPS_VellaChenStillinger_2017_Sn__MO_316045643888_002 | meam | MEAM potential for liquid Sn developed by Vella et al. (2017) v002 |
| MEAM_LAMMPS_Wagner_2007_Cu__MO_313717476091_002 | meam | MEAM potential for Cu developed by Wagner (2007) v002 |
| MEAM_LAMMPS_Wagner_2007_Ni__MO_444394830472_002 | meam | MEAM potential for Ni developed by Wagner (2007) v002 |
| MEAM_LAMMPS_Wagner_2007_SiC__MO_430846853065_002 | meam | MEAM potential for Si-C developed by Wagner (2007) v002 |
| MEAM_LAMMPS_WangOhLee_2020_CuCo__MO_694335101831_002 | meam | MEAM Potential for the Cu-Co system developed by Wang et al. (2020) v002 |
| MEAM_LAMMPS_WangOhLee_2020_CuCo__MO_849011491644_002 | meam | MEAM Potential for the Cu-Co system developed by Wang, Oh, and Lee (2020) v002 |
| MEAM_LAMMPS_WangOhLee_2020_CuMo__MO_380272712420_002 | meam | MEAM Potential for the Cu-Mo system developed by Wang, Oh, and Lee (2020) v002 |
| MEAM_LAMMPS_WangOhLee_2020_CuMo__MO_486450342170_002 | meam | MEAM Potential for the Cu-Mo system developed by Wang et al. (2020) v002 |
| MEAM_LAMMPS_WeiZhouLi_2019_BeO__MO_344044439515_002 | meam | MEAM potential for BeO structure developed by Wei et al. (2019) v002 |
| MEAM_LAMMPS_WuLeeSu_2017_NiCr__MO_880803040302_002 | meam | MEAM Potential for the Ni-Cr system developed by Wu, Lee, and Su (2017) v002 |
| MEAM_LAMMPS_WuLeeSu_2017_NiCrFe__MO_912636107108_002 | meam | MEAM Potential for the Ni-Cr-Fe system developed by Wu, Lee, and Su (2017) v002 |
| MEAM_LAMMPS_WuLeeSu_2017_NiFe__MO_321233176498_002 | meam | MEAM Potential for the Ni-Fe system developed by Wu, Lee, and Su (2017) v002 |
| MEAM_LAMMPS_YangQi_2019_Nb__MO_360068930164_002 | meam | MEAM potential for Niobium developed by Yang and Qi (2019) v002 |
| MEAM_LAMMPS_ZhangTrinkle_2016_TiO__MO_612732924171_002 | meam | MEAM potential for the Ti-O system developed by Zhang and Trinkle (2016) v002 |
| MEAM_LAMMPS_ZhouDickelBaskes_2021_Bi__MO_221877348962_001 | meam | MEAM Potential for Bi developed by Zhou et al. (2021) v001 |
| MSMEAM_Gibson_Ti__MO_309653492217_000 | meam | Titanium model for multi-state modified embedded atom method |
| Sim_LAMMPS_MEAM_AlmyrasSangiovanniSarakinos_2019_NAlTi__SM_871795249052_000 | meam | LAMMPS MEAM potential for the Ti-Al-N system developed by Almyras et al. v000 |
| Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Cu__SM_239791545509_000 | meam | LAMMPS MEAM potential for Cu developed by Asadi et al. (2015) v000 |
| Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Fe__SM_042630680993_001 | meam | LAMMPS MEAM potential for Fe developed by Asadi et al. (2015) v001 |
| Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Ni__SM_078420412697_001 | meam | LAMMPS MEAM potential for Ni developed by Asadi et al. (2015) v001 |
| Sim_LAMMPS_MEAM_CuiGaoCui_2012_LiSi__SM_562938628131_000 | meam | LAMMPS MEAM potential for Li-Si alloys developed by Cui et al. (2012) v000 |
| Sim_LAMMPS_MEAM_DuLenoskyHennig_2011_Si__SM_662785656123_000 | meam | LAMMPS Spline-based MEAM potential for Si system developed by Du et al. (2011) v000 |
| Sim_LAMMPS_MEAM_EtesamiAsadi_2018_Cu__SM_316120381362_001 | meam | LAMMPS MEAM potential for Cu developed by Etesami and Asadi (2018) v001 |
| Sim_LAMMPS_MEAM_EtesamiAsadi_2018_Fe__SM_267016608755_001 | meam | LAMMPS MEAM potential for Fe developed by Etesami and Asadi (2018) v001 |
| Sim_LAMMPS_MEAM_EtesamiAsadi_2018_Ni__SM_333792531460_001 | meam | LAMMPS MEAM potential for Ni developed by Etesami and Asadi (2018) v001 |
| Sim_LAMMPS_MEAM_FernandezPascuet_2014_U__SM_176800861722_000 | meam | LAMMPS MEAM potential for U developed by Fernández and Pascuet (2014) v000 |
| Sim_LAMMPS_MEAM_GaoOterodelaRozaAouadi_2013_AgTaO__SM_485325656366_001 | meam | LAMMPS MEAM potential for perovskite silver tantalate (AgTaO3) developed by Gao et al. (2013) v001 |
| Sim_LAMMPS_MEAM_HennigLenoskyTrinkle_2008_Ti__SM_318953488749_000 | meam | LAMMPS MEAM potential for Ti developed by Hennig et al. (2008) v000 |
| Sim_LAMMPS_MEAM_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__SM_656517352485_000 | meam | LAMMPS MEAM potential for Al-Si-Mg-Cu-Fe alloys developed by Jelinek et al. (2012) v000 |
| Sim_LAMMPS_MEAM_KimJungLee_2009_FeTiC__SM_531038274471_000 | meam | LAMMPS MEAM potential for Fe-Ti-C developed by Kim, Jung, and Lee (2009) v000 |
| Sim_LAMMPS_MEAM_KoGrabowskiNeugebauer_2015_NiTi__SM_770142935022_000 | meam | LAMMPS MEAM potential for Ni-Ti developed by Ko, Grabowski, and Neugebauer (2015) v000 |
| Sim_LAMMPS_MEAM_Lenosky_2017_W__SM_631352869360_000 | meam | LAMMPS MEAM Potential for W developed by Lenosky (2017) v000 |
| Sim_LAMMPS_MEAM_LenoskySadighAlonso_2000_Si__SM_622320990752_000 | meam | LAMMPS MEAM potential for Si system developed by Lenosky et al. (2000) v000 |
| Sim_LAMMPS_MEAM_LiyanageKimHouze_2014_FeC__SM_652425777808_001 | meam | LAMMPS MEAM potential for Fe-C developed by Liyanage et al. (2014) v001 |
| Sim_LAMMPS_MEAM_MaiselKoZhang_2017_VNiTi__SM_971529344487_000 | meam | LAMMPS MEAM potential for V-Ni-Ti developed by Maisel et al. (2017) v000 |
| Sim_LAMMPS_MEAM_ParkFellingerLenosky_2012_Mo__SM_769176993156_000 | meam | LAMMPS MEAM Potential for Mo developed by Park et al. (2012) v000 |
| Sim_LAMMPS_MEAM_ParkFellingerLenosky_2012_Ta__SM_907764821792_000 | meam | LAMMPS MEAM Potential for Ta developed by Park et al. (2012) v000 |
| Sim_LAMMPS_MEAM_ParkFellingerLenosky_2012_W__SM_163270462402_000 | meam | LAMMPS MEAM Potential for W developed by Park et al. (2012) v000 |
| Sim_LAMMPS_MEAM_PascuetFernandez_2015_Al__SM_811588957187_000 | meam | LAMMPS MEAM potential for Al developed by Pascuet and Fernandez (2015) v000 |
| Sim_LAMMPS_MEAM_PascuetFernandez_2015_AlU__SM_721930391003_000 | meam | LAMMPS MEAM potential for Al-U developed by Pascuet and Fernandez (2015) v000 |
| Sim_LAMMPS_MEAM_VellaChenStillinger_2017_Sn__SM_629915663723_000 | meam | LAMMPS MEAM potential for liquid Sn developed by Vella et al. (2017) v000 |
| Sim_LAMMPS_MEAM_Wagner_2007_Cu__SM_521856783904_000 | meam | LAMMPS MEAM potential for Cu developed by Wagner (2007) v000 |
| Sim_LAMMPS_MEAM_Wagner_2007_Ni__SM_168413969663_000 | meam | LAMMPS MEAM potential for Ni developed by Wagner (2007) v000 |
| Sim_LAMMPS_MEAM_Wagner_2007_SiC__SM_264944083668_000 | meam | LAMMPS MEAM potential for Si-C developed by Wagner (2007) v000 |
| Sim_LAMMPS_MEAM_ZhangTrinkle_2016_TiO__SM_513612626462_000 | meam | LAMMPS MEAM potential for the Ti-O system developed by Zhang and Trinkle (2016) v000 |
| Model | Type | Title |
|---|---|---|
| MFF_MistriotisFlytzanisFarantos_1989_Si__MO_080526771943_001 | mff | MFF potential for Si developed by Mistriotis, Flytzanis and Farantos (1989) v001 |
| Model | Type | Title |
|---|---|---|
| MJ_MorrisAgaLevashov_2008_Fe__MO_857282754307_003 | mj | Modified Johnson pair potential for Fe developed by Morris, Aga, and Levashov (2008) v003 |
| Model | Type | Title |
|---|---|---|
| Morse_EIP_GuthikondaElliott_2011_AuCd__MO_703849496106_002 | morse | Morse effective interaction potential for the AuCd shape-memory alloy developed by Guthikonda and Elliott (2011) v002 |
| Morse_QuinticSmoothed_Jelinek_1972_Ar__MO_908645784389_002 | morse | Morse potential (quintic smoothing) for Ar developed by Jelinek (1972) v002 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Ag__MO_111986436268_004 | morse | Morse potential (shifted) for Ag by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Al__MO_140175748626_004 | morse | Morse potential (shifted) for Al by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Ba__MO_676977998912_004 | morse | Morse potential (shifted) for Ba by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Ca__MO_159753408472_004 | morse | Morse potential (shifted) for Ca by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Cr__MO_859700307573_004 | morse | Morse potential (shifted) for Cr by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Cs__MO_187111446479_004 | morse | Morse potential (shifted) for Cs by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Cu__MO_151002396060_004 | morse | Morse potential (shifted) for Cu by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Fe__MO_147603128437_004 | morse | Morse potential (shifted) for Fe by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_K__MO_836927321152_004 | morse | Morse potential (shifted) for K by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Mo__MO_666830945336_004 | morse | Morse potential (shifted) for Mo by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Na__MO_587469264453_004 | morse | Morse potential (shifted) for Na by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Ni__MO_381861218831_004 | morse | Morse potential (shifted) for Ni by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Pb__MO_370271093517_004 | morse | Morse potential (shifted) for Pb by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Rb__MO_908110223949_004 | morse | Morse potential (shifted) for Rb by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Sr__MO_497591319122_004 | morse | Morse potential (shifted) for Sr by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_W__MO_646516726498_004 | morse | Morse potential (shifted) for W by Girifalco and Weizer (1959) using a high-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Ag__MO_137719994600_004 | morse | Morse potential (shifted) for Ag by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Al__MO_411898953661_004 | morse | Morse potential (shifted) for Al by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Ba__MO_143487634619_004 | morse | Morse potential (shifted) for Ba by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Ca__MO_887105884651_004 | morse | Morse potential (shifted) for Ca by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Cr__MO_483480726117_004 | morse | Morse potential (shifted) for Cr by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Cs__MO_256406354561_004 | morse | Morse potential (shifted) for Cs by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Cu__MO_673777079812_004 | morse | Morse potential (shifted) for Cu by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Fe__MO_331285495617_004 | morse | Morse potential (shifted) for Fe by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_K__MO_749732139672_004 | morse | Morse potential (shifted) for K by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Mo__MO_228581001644_004 | morse | Morse potential (shifted) for Mo by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Na__MO_707981543254_004 | morse | Morse potential (shifted) for Na by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Ni__MO_322509103239_004 | morse | Morse potential (shifted) for Ni by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Pb__MO_534638645497_004 | morse | Morse potential (shifted) for Pb by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Rb__MO_754498969542_004 | morse | Morse potential (shifted) for Rb by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_Sr__MO_801083489225_004 | morse | Morse potential (shifted) for Sr by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959LowCutoff_W__MO_489351836217_004 | morse | Morse potential (shifted) for W by Girifalco and Weizer (1959) using a low-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Ag__MO_861893969202_004 | morse | Morse potential (shifted) for Ag by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Al__MO_279544746097_004 | morse | Morse potential (shifted) for Al by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Ba__MO_229241184339_004 | morse | Morse potential (shifted) for Ba by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Ca__MO_562200212426_004 | morse | Morse potential (shifted) for Ca by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Cr__MO_245813471114_004 | morse | Morse potential (shifted) for Cr by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Cs__MO_999639780744_004 | morse | Morse potential (shifted) for Cs by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Cu__MO_173787283511_004 | morse | Morse potential (shifted) for Cu by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Fe__MO_984358344196_004 | morse | Morse potential (shifted) for Fe by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_K__MO_202712315930_004 | morse | Morse potential (shifted) for K by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Mo__MO_534363225491_004 | morse | Morse potential (shifted) for Mo by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Na__MO_636041334617_004 | morse | Morse potential (shifted) for Na by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Ni__MO_758825945924_004 | morse | Morse potential (shifted) for Ni by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Pb__MO_958424213898_004 | morse | Morse potential (shifted) for Pb by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Rb__MO_147245690895_004 | morse | Morse potential (shifted) for Rb by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_Sr__MO_964297938209_004 | morse | Morse potential (shifted) for Sr by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_GirifalcoWeizer_1959MedCutoff_W__MO_390128289865_004 | morse | Morse potential (shifted) for W by Girifalco and Weizer (1959) using a medium-accuracy cutoff distance v004 |
| Morse_Shifted_Glyde_1970_Ne__MO_169434419764_004 | morse | Morse potential (shifted) for Ne developed by Glyde (1970) v004 |
| Morse_Shifted_Jelinek_1972_Ar__MO_831902330215_004 | morse | Morse potential (shifted) for Ar by Jelinek (1972) v004 |
| Morse_SigmoidalSmoothed_Jelinek_1972_Ar__MO_071460865933_002 | morse | Morse potential (sigmoidal smoothing) for Ar developed by Jelinek (1972) v002 |
| Pair_Morse_Modified_MacDonaldMacDonald_Cu__MO_034823476734_000 | morse | Modified Morse pair potential for copper due to MacDonald and MacDonald |
| Model | Type | Title |
|---|---|---|
| TorchML_NequIP_GuptaTadmorMartiniani_2024_Si__MO_196181738937_001 | nequip | Parallel NequIP Equivariant GNN for Si developed by Gupta et al. (2024) v001 |
| Model | Type | Title |
|---|---|---|
| PolyMLP_Seko_2022p1_AgAu__MO_823011623436_000 | polymlp | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgBa__MO_276055717124_000 | polymlp | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgIn__MO_034422676833_000 | polymlp | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgLa__MO_772792233950_000 | polymlp | Polynomial machine learning potential for Ag-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgPb__MO_648223078455_000 | polymlp | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgSn__MO_101110678269_000 | polymlp | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlAg__MO_350112129763_000 | polymlp | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlAu__MO_773361983100_000 | polymlp | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlBa__MO_555884071866_000 | polymlp | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlCa__MO_876726883487_000 | polymlp | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlCu__MO_665550932377_000 | polymlp | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlGa__MO_483523650549_000 | polymlp | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlGe__MO_727636380924_000 | polymlp | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlIn__MO_713803919996_000 | polymlp | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlK__MO_735281460727_000 | polymlp | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlLa__MO_996217515025_000 | polymlp | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlNb__MO_335935516233_000 | polymlp | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlPb__MO_560806804746_000 | polymlp | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlPt__MO_028787619252_000 | polymlp | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlSc__MO_249690680481_000 | polymlp | Polynomial machine learning potential for Al-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlSi__MO_772463641678_000 | polymlp | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlSn__MO_036654852882_000 | polymlp | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlSr__MO_948606927949_000 | polymlp | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlV__MO_065088101597_000 | polymlp | Polynomial machine learning potential for Al-V developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlY__MO_936457231816_000 | polymlp | Polynomial machine learning potential for Al-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlZn__MO_683258743000_000 | polymlp | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlZr__MO_937296258990_000 | polymlp | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_BeAl__MO_847694853332_000 | polymlp | Polynomial machine learning potential for Be-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_BeGe__MO_396933178400_000 | polymlp | Polynomial machine learning potential for Be-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_BeSi__MO_788283766723_000 | polymlp | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_BeSn__MO_314058940376_000 | polymlp | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaAg__MO_595358060477_000 | polymlp | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaAu__MO_732840578400_000 | polymlp | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaCu__MO_017053542787_000 | polymlp | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaGa__MO_005649812247_000 | polymlp | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaGe__MO_803284128474_000 | polymlp | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaIn__MO_267537996802_000 | polymlp | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaLa__MO_879970448875_000 | polymlp | Polynomial machine learning potential for Ca-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaPb__MO_943415111691_000 | polymlp | Polynomial machine learning potential for Ca-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaSc__MO_367705290077_000 | polymlp | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaSn__MO_351332362877_000 | polymlp | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaTi__MO_149740904039_000 | polymlp | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaZn__MO_840339866631_000 | polymlp | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CuAg__MO_537857072278_000 | polymlp | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CuAu__MO_823062543360_000 | polymlp | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CuZn__MO_795242620384_000 | polymlp | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GaAg__MO_212356614349_000 | polymlp | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GaSn__MO_067490656712_000 | polymlp | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GeIn__MO_567938768570_000 | polymlp | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GePb__MO_391827440225_000 | polymlp | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GeSn__MO_544175726222_000 | polymlp | Polynomial machine learning potential for Ge-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GeSr__MO_054998251352_000 | polymlp | Polynomial machine learning potential for Ge-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KAg__MO_199727096976_000 | polymlp | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KAu__MO_356560124445_000 | polymlp | Polynomial machine learning potential for K-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KCu__MO_255099340995_000 | polymlp | Polynomial machine learning potential for K-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KGe__MO_459655813835_000 | polymlp | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KIn__MO_924892044915_000 | polymlp | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KPb__MO_720100563535_000 | polymlp | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KSn__MO_076821718242_000 | polymlp | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KZn__MO_321402413208_000 | polymlp | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiAg__MO_001132182851_000 | polymlp | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiAl__MO_080005454372_000 | polymlp | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiAu__MO_040937774580_000 | polymlp | Polynomial machine learning potential for Li-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiBa__MO_773440616906_000 | polymlp | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiBe__MO_981139059532_000 | polymlp | Polynomial machine learning potential for Li-Be developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiBi__MO_112699923754_000 | polymlp | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiCa__MO_384169544942_000 | polymlp | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiCu__MO_053552052173_000 | polymlp | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiGa__MO_483161604769_000 | polymlp | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiGe__MO_911566550468_000 | polymlp | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiIn__MO_761614630215_000 | polymlp | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiMg__MO_199365257743_000 | polymlp | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiPb__MO_466687827018_000 | polymlp | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiSi__MO_246471043381_000 | polymlp | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiSn__MO_081059051967_000 | polymlp | Polynomial machine learning potential for Li-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiSr__MO_554122036474_000 | polymlp | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiTi__MO_671224571016_000 | polymlp | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiZn__MO_783498026982_000 | polymlp | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgAg__MO_643231797558_000 | polymlp | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgAl__MO_670946385478_000 | polymlp | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgAu__MO_142560144114_000 | polymlp | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgCa__MO_874169223165_000 | polymlp | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgCu__MO_683438982122_000 | polymlp | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgGa__MO_549948643305_000 | polymlp | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgGe__MO_534496340093_000 | polymlp | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgIn__MO_723240239962_000 | polymlp | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgK__MO_994423577393_000 | polymlp | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgSc__MO_471453066610_000 | polymlp | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgSi__MO_764015570853_000 | polymlp | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgSn__MO_110075421146_000 | polymlp | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgTi__MO_989121378877_000 | polymlp | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgY__MO_471276375465_000 | polymlp | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaAl__MO_354202555176_000 | polymlp | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaGa__MO_298444110548_000 | polymlp | Polynomial machine learning potential for Na-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaGe__MO_547006368556_000 | polymlp | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaIn__MO_587982479968_000 | polymlp | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaMg__MO_586851152483_000 | polymlp | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaPb__MO_543960217468_000 | polymlp | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaSi__MO_512180175980_000 | polymlp | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaSn__MO_294901016081_000 | polymlp | Polynomial machine learning potential for Na-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaSr__MO_364777300293_000 | polymlp | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_PdPt__MO_064180783338_000 | polymlp | Polynomial machine learning potential for Pd-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiAg__MO_558818721890_000 | polymlp | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiAu__MO_033687705195_000 | polymlp | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiCu__MO_009960336295_000 | polymlp | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiGa__MO_959459596539_000 | polymlp | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiGe__MO_462952553615_000 | polymlp | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiIn__MO_197188842574_000 | polymlp | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiPb__MO_963333486681_000 | polymlp | Polynomial machine learning potential for Si-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiSn__MO_118151700862_000 | polymlp | Polynomial machine learning potential for Si-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiZn__MO_902337818624_000 | polymlp | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SnPb__MO_340633106548_000 | polymlp | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SrPb__MO_381597336914_000 | polymlp | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_TiAl__MO_223894196126_000 | polymlp | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_TiGe__MO_025114491917_000 | polymlp | Polynomial machine learning potential for Ti-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnAg__MO_002780648259_000 | polymlp | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnAu__MO_656935464156_000 | polymlp | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnGa__MO_203911770964_000 | polymlp | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnGe__MO_072257033094_000 | polymlp | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnIn__MO_258915657943_000 | polymlp | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnSn__MO_516198383864_000 | polymlp | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgAu__MO_944402137439_000 | polymlp | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgBa__MO_857123287577_000 | polymlp | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgIn__MO_702096149594_000 | polymlp | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgLa__MO_409439888675_000 | polymlp | Polynomial machine learning potential for Ag-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgPb__MO_595083573723_000 | polymlp | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgSn__MO_210942986619_000 | polymlp | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlAg__MO_540932853733_000 | polymlp | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlAu__MO_200707653568_000 | polymlp | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlBa__MO_215780095507_000 | polymlp | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlCa__MO_887341599634_000 | polymlp | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlCu__MO_933443908371_000 | polymlp | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlGa__MO_031943987658_000 | polymlp | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlGe__MO_788236841133_000 | polymlp | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlIn__MO_750879363282_000 | polymlp | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlK__MO_359877761911_000 | polymlp | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlLa__MO_031192979633_000 | polymlp | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlNb__MO_344619612839_000 | polymlp | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlPb__MO_640728266160_000 | polymlp | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlPt__MO_114321754774_000 | polymlp | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlSc__MO_975921301943_000 | polymlp | Polynomial machine learning potential for Al-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlSi__MO_795457664825_000 | polymlp | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlSn__MO_726394842236_000 | polymlp | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlSr__MO_233853181609_000 | polymlp | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlV__MO_021520966414_000 | polymlp | Polynomial machine learning potential for Al-V developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlY__MO_605437015539_000 | polymlp | Polynomial machine learning potential for Al-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlZn__MO_194371430688_000 | polymlp | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlZr__MO_403991319854_000 | polymlp | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_BeAl__MO_321074169214_000 | polymlp | Polynomial machine learning potential for Be-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_BeGe__MO_817754113288_000 | polymlp | Polynomial machine learning potential for Be-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_BeSi__MO_262355795611_000 | polymlp | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_BeSn__MO_649082660934_000 | polymlp | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaAg__MO_808961412194_000 | polymlp | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaAu__MO_597464483809_000 | polymlp | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaCu__MO_274542225214_000 | polymlp | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaGa__MO_576362609785_000 | polymlp | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaGe__MO_751203544778_000 | polymlp | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaIn__MO_929722546386_000 | polymlp | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaLa__MO_112910335336_000 | polymlp | Polynomial machine learning potential for Ca-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaPb__MO_233509421447_000 | polymlp | Polynomial machine learning potential for Ca-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaSc__MO_965847074706_000 | polymlp | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaSn__MO_313708305794_000 | polymlp | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaTi__MO_183166784696_000 | polymlp | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaZn__MO_222201343732_000 | polymlp | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CuAg__MO_495090193440_000 | polymlp | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CuAu__MO_864476668504_000 | polymlp | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CuZn__MO_352663723076_000 | polymlp | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GaAg__MO_246275243138_000 | polymlp | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GaSn__MO_673995264882_000 | polymlp | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GeIn__MO_884720655346_000 | polymlp | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GePb__MO_675520716098_000 | polymlp | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GeSn__MO_877602326315_000 | polymlp | Polynomial machine learning potential for Ge-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GeSr__MO_777727202802_000 | polymlp | Polynomial machine learning potential for Ge-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KAg__MO_875335959983_000 | polymlp | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KAu__MO_929949809109_000 | polymlp | Polynomial machine learning potential for K-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KCu__MO_192585302435_000 | polymlp | Polynomial machine learning potential for K-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KGe__MO_722132851668_000 | polymlp | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KIn__MO_362211213503_000 | polymlp | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KPb__MO_806670238256_000 | polymlp | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KSn__MO_952660254314_000 | polymlp | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KZn__MO_027391677821_000 | polymlp | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiAg__MO_185411762954_000 | polymlp | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiAl__MO_960827550644_000 | polymlp | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiAu__MO_940868595740_000 | polymlp | Polynomial machine learning potential for Li-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiBa__MO_286683749335_000 | polymlp | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiBe__MO_157152668775_000 | polymlp | Polynomial machine learning potential for Li-Be developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiBi__MO_314321973529_000 | polymlp | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiCa__MO_941364692155_000 | polymlp | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiCu__MO_071714769833_000 | polymlp | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiGa__MO_496584090412_000 | polymlp | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiGe__MO_760725313082_000 | polymlp | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiIn__MO_876031080716_000 | polymlp | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiMg__MO_588232939920_000 | polymlp | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiPb__MO_621271114150_000 | polymlp | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiSi__MO_119176603929_000 | polymlp | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiSn__MO_572938093835_000 | polymlp | Polynomial machine learning potential for Li-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiSr__MO_781397296555_000 | polymlp | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiTi__MO_995825976587_000 | polymlp | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiZn__MO_574727852694_000 | polymlp | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgAg__MO_350904051925_000 | polymlp | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgAl__MO_644896580510_000 | polymlp | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgAu__MO_594489219664_000 | polymlp | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgCa__MO_204288236461_000 | polymlp | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgCu__MO_776403086657_000 | polymlp | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgGa__MO_267258848031_000 | polymlp | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgGe__MO_559191134710_000 | polymlp | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgIn__MO_319608573159_000 | polymlp | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgK__MO_244539768462_000 | polymlp | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgSc__MO_532956650898_000 | polymlp | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgSi__MO_713701275105_000 | polymlp | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgSn__MO_827102424057_000 | polymlp | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgTi__MO_216584073599_000 | polymlp | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgY__MO_181738427501_000 | polymlp | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaAl__MO_672850893186_000 | polymlp | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaGa__MO_994323063865_000 | polymlp | Polynomial machine learning potential for Na-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaGe__MO_332071011861_000 | polymlp | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaIn__MO_145634060849_000 | polymlp | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaMg__MO_416243651908_000 | polymlp | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaPb__MO_069280874556_000 | polymlp | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaSi__MO_021552775560_000 | polymlp | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaSn__MO_422482847539_000 | polymlp | Polynomial machine learning potential for Na-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaSr__MO_337323093842_000 | polymlp | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_PdPt__MO_521848844432_000 | polymlp | Polynomial machine learning potential for Pd-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiAg__MO_132921485586_000 | polymlp | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiAu__MO_885747768449_000 | polymlp | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiCu__MO_103337519975_000 | polymlp | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiGa__MO_729188142180_000 | polymlp | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiGe__MO_816965513628_000 | polymlp | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiIn__MO_916358924324_000 | polymlp | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiPb__MO_990976689374_000 | polymlp | Polynomial machine learning potential for Si-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiSn__MO_367791721365_000 | polymlp | Polynomial machine learning potential for Si-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiZn__MO_981137873400_000 | polymlp | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SnPb__MO_044027005325_000 | polymlp | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SrPb__MO_573083564162_000 | polymlp | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_TiAl__MO_578636983448_000 | polymlp | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_TiGe__MO_799222074320_000 | polymlp | Polynomial machine learning potential for Ti-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnAg__MO_305154668187_000 | polymlp | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnAu__MO_767971944157_000 | polymlp | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnGa__MO_724449532698_000 | polymlp | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnGe__MO_936651575259_000 | polymlp | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnIn__MO_566847094064_000 | polymlp | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnSn__MO_543973400203_000 | polymlp | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgAu__MO_379712378791_000 | polymlp | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgBa__MO_507856124860_000 | polymlp | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgIn__MO_098332856981_000 | polymlp | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgLa__MO_451410538946_000 | polymlp | Polynomial machine learning potential for Ag-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgPb__MO_028613465923_000 | polymlp | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgSn__MO_389004779206_000 | polymlp | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlAg__MO_773586197956_000 | polymlp | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlAu__MO_044790454312_000 | polymlp | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlBa__MO_079128391049_000 | polymlp | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlCa__MO_263974011241_000 | polymlp | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlCu__MO_133987906701_000 | polymlp | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlGa__MO_679544221140_000 | polymlp | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlGe__MO_658352597054_000 | polymlp | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlIn__MO_069707644416_000 | polymlp | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlK__MO_508800702371_000 | polymlp | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlLa__MO_309404032314_000 | polymlp | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlNb__MO_469837818614_000 | polymlp | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlPb__MO_979853256057_000 | polymlp | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlPt__MO_825557510061_000 | polymlp | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlSc__MO_502197185646_000 | polymlp | Polynomial machine learning potential for Al-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlSi__MO_466510930009_000 | polymlp | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlSn__MO_609660235135_000 | polymlp | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlSr__MO_681566610227_000 | polymlp | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlY__MO_531083567860_000 | polymlp | Polynomial machine learning potential for Al-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlZn__MO_535776581463_000 | polymlp | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlZr__MO_492687405328_000 | polymlp | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_BeAl__MO_846123031688_000 | polymlp | Polynomial machine learning potential for Be-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_BeGe__MO_521014392699_000 | polymlp | Polynomial machine learning potential for Be-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_BeSi__MO_255226158073_000 | polymlp | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_BeSn__MO_951792558467_000 | polymlp | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaAg__MO_724078251814_000 | polymlp | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaAu__MO_967921588432_000 | polymlp | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaCu__MO_380370949024_000 | polymlp | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaGa__MO_322998972979_000 | polymlp | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaGe__MO_997859060795_000 | polymlp | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaIn__MO_365720691370_000 | polymlp | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaLa__MO_082791912842_000 | polymlp | Polynomial machine learning potential for Ca-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaPb__MO_887856246185_000 | polymlp | Polynomial machine learning potential for Ca-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaSc__MO_556957522634_000 | polymlp | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaSn__MO_801669282358_000 | polymlp | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaTi__MO_237846126333_000 | polymlp | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaZn__MO_572892941444_000 | polymlp | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CuAg__MO_344462201016_000 | polymlp | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CuAu__MO_716806330835_000 | polymlp | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CuZn__MO_302853281913_000 | polymlp | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GaAg__MO_577752152732_000 | polymlp | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GaSn__MO_639698572776_000 | polymlp | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GeIn__MO_640570535503_000 | polymlp | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GePb__MO_131114616619_000 | polymlp | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GeSn__MO_912745532992_000 | polymlp | Polynomial machine learning potential for Ge-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KAg__MO_886262818219_000 | polymlp | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KAu__MO_086478926031_000 | polymlp | Polynomial machine learning potential for K-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KCu__MO_169026024582_000 | polymlp | Polynomial machine learning potential for K-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KGe__MO_555348570218_000 | polymlp | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KIn__MO_719208417667_000 | polymlp | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KPb__MO_827816257398_000 | polymlp | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KSn__MO_490581159197_000 | polymlp | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KZn__MO_217149617540_000 | polymlp | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiAg__MO_302731467895_000 | polymlp | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiAl__MO_056718076690_000 | polymlp | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiAu__MO_072929907452_000 | polymlp | Polynomial machine learning potential for Li-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiBa__MO_522523401676_000 | polymlp | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiBe__MO_007700728408_000 | polymlp | Polynomial machine learning potential for Li-Be developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiBi__MO_058150661474_000 | polymlp | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiCa__MO_127854321970_000 | polymlp | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiCu__MO_161493229607_000 | polymlp | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiGa__MO_911736184845_000 | polymlp | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiGe__MO_602908061110_000 | polymlp | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiIn__MO_366749846814_000 | polymlp | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiMg__MO_837858250320_000 | polymlp | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiPb__MO_300447529744_000 | polymlp | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiSi__MO_538467579588_000 | polymlp | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiSn__MO_291911517283_000 | polymlp | Polynomial machine learning potential for Li-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiSr__MO_240370039202_000 | polymlp | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiTi__MO_971492312043_000 | polymlp | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiZn__MO_776440448715_000 | polymlp | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgAg__MO_964339722169_000 | polymlp | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgAl__MO_744396195472_000 | polymlp | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgAu__MO_948981209922_000 | polymlp | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgCa__MO_483959833126_000 | polymlp | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgCu__MO_719412165638_000 | polymlp | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgGa__MO_503838648358_000 | polymlp | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgGe__MO_108494656671_000 | polymlp | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgIn__MO_028623231466_000 | polymlp | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgK__MO_510807505506_000 | polymlp | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgSc__MO_340958950771_000 | polymlp | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgSi__MO_812147479425_000 | polymlp | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgSn__MO_254649659157_000 | polymlp | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgTi__MO_727052546981_000 | polymlp | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgY__MO_950072061157_000 | polymlp | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaAl__MO_679720458023_000 | polymlp | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaGa__MO_506336950147_000 | polymlp | Polynomial machine learning potential for Na-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaGe__MO_404910834284_000 | polymlp | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaIn__MO_390744351074_000 | polymlp | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaMg__MO_535937059130_000 | polymlp | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaPb__MO_964178395930_000 | polymlp | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaSi__MO_801816315004_000 | polymlp | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaSn__MO_507154403588_000 | polymlp | Polynomial machine learning potential for Na-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaSr__MO_036658387001_000 | polymlp | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_PdPt__MO_397668178553_000 | polymlp | Polynomial machine learning potential for Pd-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiAg__MO_917105717444_000 | polymlp | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiAu__MO_173801784889_000 | polymlp | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiCu__MO_393479569734_000 | polymlp | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiGa__MO_198498705046_000 | polymlp | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiGe__MO_699353865120_000 | polymlp | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiIn__MO_812549533556_000 | polymlp | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiPb__MO_528321941482_000 | polymlp | Polynomial machine learning potential for Si-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiSn__MO_009807584020_000 | polymlp | Polynomial machine learning potential for Si-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiZn__MO_030993907682_000 | polymlp | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SnPb__MO_536989370303_000 | polymlp | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SrPb__MO_824270967893_000 | polymlp | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_TiAl__MO_945531783513_000 | polymlp | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_TiGe__MO_607202239115_000 | polymlp | Polynomial machine learning potential for Ti-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnAg__MO_283932177215_000 | polymlp | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnAu__MO_613106811566_000 | polymlp | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnGa__MO_605652434522_000 | polymlp | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnGe__MO_943988361552_000 | polymlp | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnIn__MO_935571150853_000 | polymlp | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnSn__MO_361197325697_000 | polymlp | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgAu__MO_920701415341_000 | polymlp | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgBa__MO_194579513066_000 | polymlp | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgIn__MO_266812879859_000 | polymlp | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgPb__MO_060209960188_000 | polymlp | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgSn__MO_551463947779_000 | polymlp | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlAg__MO_575237573187_000 | polymlp | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlAu__MO_957328769065_000 | polymlp | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlBa__MO_197659268820_000 | polymlp | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlCa__MO_658245269236_000 | polymlp | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlCu__MO_683961731173_000 | polymlp | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlGa__MO_753352975874_000 | polymlp | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlGe__MO_568124972726_000 | polymlp | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlIn__MO_944184790819_000 | polymlp | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlK__MO_412941221384_000 | polymlp | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlLa__MO_926776289321_000 | polymlp | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlNb__MO_632661817177_000 | polymlp | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlPb__MO_770521697914_000 | polymlp | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlPt__MO_863302581933_000 | polymlp | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlSi__MO_722793085759_000 | polymlp | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlSn__MO_170495204252_000 | polymlp | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlSr__MO_461454518609_000 | polymlp | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlY__MO_299813494955_000 | polymlp | Polynomial machine learning potential for Al-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlZn__MO_394039619756_000 | polymlp | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlZr__MO_733064316353_000 | polymlp | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_BeAl__MO_166223079721_000 | polymlp | Polynomial machine learning potential for Be-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_BeGe__MO_210562250655_000 | polymlp | Polynomial machine learning potential for Be-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_BeSi__MO_671634227239_000 | polymlp | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_BeSn__MO_528555971601_000 | polymlp | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaAg__MO_840510785458_000 | polymlp | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaAu__MO_051446997049_000 | polymlp | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaCu__MO_494823132535_000 | polymlp | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaGa__MO_620107049977_000 | polymlp | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaGe__MO_309096382726_000 | polymlp | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaIn__MO_490765539946_000 | polymlp | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaPb__MO_672167774288_000 | polymlp | Polynomial machine learning potential for Ca-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaSc__MO_604912657616_000 | polymlp | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaSn__MO_940919591360_000 | polymlp | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaTi__MO_099193110426_000 | polymlp | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaZn__MO_058393404652_000 | polymlp | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CuAg__MO_810898131462_000 | polymlp | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CuAu__MO_641442516463_000 | polymlp | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CuZn__MO_364021352323_000 | polymlp | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GaAg__MO_847495365448_000 | polymlp | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GaSn__MO_724605405774_000 | polymlp | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GeIn__MO_615305547031_000 | polymlp | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GePb__MO_263929759533_000 | polymlp | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GeSn__MO_633956130614_000 | polymlp | Polynomial machine learning potential for Ge-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KAg__MO_912739399567_000 | polymlp | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KAu__MO_763201557333_000 | polymlp | Polynomial machine learning potential for K-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KCu__MO_788133924271_000 | polymlp | Polynomial machine learning potential for K-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KGe__MO_621167263450_000 | polymlp | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KIn__MO_201956052069_000 | polymlp | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KPb__MO_321952889826_000 | polymlp | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KSn__MO_763046237332_000 | polymlp | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KZn__MO_560772228117_000 | polymlp | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiAg__MO_678095293167_000 | polymlp | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiAl__MO_673249139557_000 | polymlp | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiAu__MO_212832054512_000 | polymlp | Polynomial machine learning potential for Li-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiBa__MO_747079893963_000 | polymlp | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiBi__MO_891618600784_000 | polymlp | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiCa__MO_003324915667_000 | polymlp | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiCu__MO_359470205167_000 | polymlp | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiGa__MO_015586567903_000 | polymlp | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiGe__MO_727780094789_000 | polymlp | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiIn__MO_254197704008_000 | polymlp | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiMg__MO_967814962810_000 | polymlp | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiPb__MO_712238882822_000 | polymlp | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiSi__MO_249506249457_000 | polymlp | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiSr__MO_624269504264_000 | polymlp | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiTi__MO_250427208520_000 | polymlp | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiZn__MO_731428135000_000 | polymlp | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgAg__MO_846084561202_000 | polymlp | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgAl__MO_692135414275_000 | polymlp | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgAu__MO_609770801376_000 | polymlp | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgCa__MO_375704627266_000 | polymlp | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgCu__MO_972374266607_000 | polymlp | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgGa__MO_559397211025_000 | polymlp | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgGe__MO_975215938643_000 | polymlp | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgIn__MO_788672201407_000 | polymlp | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgK__MO_772460227899_000 | polymlp | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgSc__MO_491249768216_000 | polymlp | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgSi__MO_191149570852_000 | polymlp | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgSn__MO_044676933142_000 | polymlp | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgTi__MO_572593306702_000 | polymlp | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgY__MO_261056054736_000 | polymlp | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaAl__MO_304352549753_000 | polymlp | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaGa__MO_029516867574_000 | polymlp | Polynomial machine learning potential for Na-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaGe__MO_227144102853_000 | polymlp | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaIn__MO_101258978335_000 | polymlp | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaMg__MO_849680225944_000 | polymlp | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaPb__MO_997133712697_000 | polymlp | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaSi__MO_443795178180_000 | polymlp | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaSn__MO_330957285625_000 | polymlp | Polynomial machine learning potential for Na-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaSr__MO_890525547758_000 | polymlp | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_PdPt__MO_840290619865_000 | polymlp | Polynomial machine learning potential for Pd-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiAg__MO_793216813105_000 | polymlp | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiAu__MO_356624561519_000 | polymlp | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiCu__MO_360657020498_000 | polymlp | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiGa__MO_055569328296_000 | polymlp | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiGe__MO_208231994609_000 | polymlp | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiIn__MO_610594020340_000 | polymlp | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiPb__MO_293461895034_000 | polymlp | Polynomial machine learning potential for Si-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiZn__MO_220027446851_000 | polymlp | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SnPb__MO_565983711337_000 | polymlp | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SrPb__MO_583185896526_000 | polymlp | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_TiAl__MO_804233947597_000 | polymlp | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnAg__MO_919349380638_000 | polymlp | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnAu__MO_032912808265_000 | polymlp | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnGa__MO_775572504262_000 | polymlp | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnGe__MO_081372807655_000 | polymlp | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnIn__MO_271291203301_000 | polymlp | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnSn__MO_373485141821_000 | polymlp | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgAu__MO_307904218441_000 | polymlp | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgBa__MO_274189772904_000 | polymlp | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgIn__MO_955686244929_000 | polymlp | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgPb__MO_210258361524_000 | polymlp | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgSn__MO_647213859199_000 | polymlp | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlAg__MO_544528803287_000 | polymlp | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlAu__MO_560298104292_000 | polymlp | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlBa__MO_239385410084_000 | polymlp | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlCa__MO_785942179170_000 | polymlp | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlCu__MO_492256155760_000 | polymlp | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlGa__MO_064268417216_000 | polymlp | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlGe__MO_195615071888_000 | polymlp | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlIn__MO_892901609085_000 | polymlp | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlK__MO_117036128203_000 | polymlp | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlLa__MO_054836116096_000 | polymlp | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlNb__MO_908619964765_000 | polymlp | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlPb__MO_514693435223_000 | polymlp | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlPt__MO_691428312553_000 | polymlp | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlSi__MO_357803532528_000 | polymlp | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlSn__MO_117347119998_000 | polymlp | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlSr__MO_827887147173_000 | polymlp | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlZn__MO_049622537243_000 | polymlp | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlZr__MO_896029608934_000 | polymlp | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_BeSi__MO_263484070173_000 | polymlp | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_BeSn__MO_878280291994_000 | polymlp | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaAg__MO_380064591060_000 | polymlp | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaAu__MO_832102410842_000 | polymlp | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaCu__MO_551804561646_000 | polymlp | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaGa__MO_713738845752_000 | polymlp | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaGe__MO_269118185064_000 | polymlp | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaIn__MO_271930275971_000 | polymlp | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaSc__MO_740127206303_000 | polymlp | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaSn__MO_785314086751_000 | polymlp | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaTi__MO_187618056597_000 | polymlp | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaZn__MO_980126476271_000 | polymlp | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CuAg__MO_782279666022_000 | polymlp | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CuAu__MO_095129455988_000 | polymlp | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CuZn__MO_956678513832_000 | polymlp | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_GaAg__MO_561805428054_000 | polymlp | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_GaSn__MO_581276203955_000 | polymlp | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_GeIn__MO_302678710789_000 | polymlp | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_GePb__MO_777051957569_000 | polymlp | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KAg__MO_846472502163_000 | polymlp | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KGe__MO_189738871515_000 | polymlp | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KIn__MO_954533725355_000 | polymlp | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KPb__MO_139125413108_000 | polymlp | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KSn__MO_918677191691_000 | polymlp | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KZn__MO_959478076724_000 | polymlp | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiAg__MO_268197378794_000 | polymlp | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiAl__MO_986302882939_000 | polymlp | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiBa__MO_257932235674_000 | polymlp | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiBi__MO_473751855524_000 | polymlp | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiCa__MO_412852556834_000 | polymlp | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiCu__MO_145767057091_000 | polymlp | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiGa__MO_497030060102_000 | polymlp | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiGe__MO_804320610366_000 | polymlp | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiIn__MO_371877881952_000 | polymlp | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiMg__MO_605808283721_000 | polymlp | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiPb__MO_540538979060_000 | polymlp | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiSi__MO_320159332794_000 | polymlp | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiSr__MO_983192719346_000 | polymlp | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiTi__MO_326969927623_000 | polymlp | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiZn__MO_350804046613_000 | polymlp | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgAg__MO_831475505025_000 | polymlp | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgAl__MO_229599966843_000 | polymlp | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgAu__MO_321125562280_000 | polymlp | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgCa__MO_379949544448_000 | polymlp | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgCu__MO_618366915582_000 | polymlp | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgGa__MO_008045565605_000 | polymlp | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgGe__MO_304107968538_000 | polymlp | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgIn__MO_948653803149_000 | polymlp | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgK__MO_902620543960_000 | polymlp | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgSc__MO_718368079956_000 | polymlp | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgSi__MO_994949789313_000 | polymlp | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgSn__MO_918607134170_000 | polymlp | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgTi__MO_848584635322_000 | polymlp | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgY__MO_682292222653_000 | polymlp | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaAl__MO_662966024141_000 | polymlp | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaGe__MO_381273201626_000 | polymlp | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaIn__MO_398378879035_000 | polymlp | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaMg__MO_916731735714_000 | polymlp | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaPb__MO_873954052557_000 | polymlp | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaSi__MO_888996916199_000 | polymlp | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaSr__MO_550486674595_000 | polymlp | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiAg__MO_112580291189_000 | polymlp | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiAu__MO_770592401017_000 | polymlp | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiCu__MO_367629511331_000 | polymlp | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiGa__MO_673957672220_000 | polymlp | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiGe__MO_346818603547_000 | polymlp | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiIn__MO_486299538452_000 | polymlp | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiZn__MO_932217968946_000 | polymlp | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SnPb__MO_377488288012_000 | polymlp | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SrPb__MO_163017302633_000 | polymlp | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_TiAl__MO_155544599291_000 | polymlp | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnAg__MO_834156792294_000 | polymlp | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnAu__MO_491151589226_000 | polymlp | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnGa__MO_909148083054_000 | polymlp | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnGe__MO_571403459169_000 | polymlp | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnIn__MO_657334591346_000 | polymlp | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnSn__MO_851574781118_000 | polymlp | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2024p1_Ag__MO_278758176027_000 | polymlp | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Al__MO_599924558257_000 | polymlp | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_As__MO_078011186186_000 | polymlp | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Au__MO_664098208305_000 | polymlp | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ba__MO_132467418614_000 | polymlp | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Be__MO_394841040550_000 | polymlp | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Bi__MO_710413125287_000 | polymlp | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ca__MO_420808095047_000 | polymlp | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Cd__MO_706111024155_000 | polymlp | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Cr__MO_691920933145_000 | polymlp | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Cs__MO_714621609752_000 | polymlp | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Cu__MO_086541143659_000 | polymlp | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_CuAgAu__MO_148623624249_000 | polymlp | Polynomial machine learning potential for Cu-Ag-Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ga__MO_443878836603_000 | polymlp | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ge__MO_431581350373_000 | polymlp | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Hf__MO_334350104497_000 | polymlp | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Hg__MO_202451644950_000 | polymlp | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_In__MO_245201429139_000 | polymlp | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ir__MO_664668386247_000 | polymlp | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_K__MO_199232334901_000 | polymlp | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_La__MO_115406610268_000 | polymlp | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Li__MO_405753048966_000 | polymlp | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Mg__MO_483087121724_000 | polymlp | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Mo__MO_817818730673_000 | polymlp | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Na__MO_325879443078_000 | polymlp | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Nb__MO_502102785787_000 | polymlp | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Os__MO_811573570744_000 | polymlp | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_P__MO_194597553958_000 | polymlp | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Pb__MO_326037826897_000 | polymlp | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Pd__MO_420470599071_000 | polymlp | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Pt__MO_577251643083_000 | polymlp | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Rb__MO_390732290131_000 | polymlp | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Re__MO_553427721950_000 | polymlp | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Rh__MO_181269649110_000 | polymlp | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ru__MO_038250921060_000 | polymlp | Polynomial machine learning potential for Ru developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Sc__MO_059205513959_000 | polymlp | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Si__MO_761568271122_000 | polymlp | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Sn__MO_129294300331_000 | polymlp | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Sr__MO_706097448505_000 | polymlp | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ta__MO_153972066617_000 | polymlp | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Te__MO_841124144524_000 | polymlp | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ti__MO_266466295447_000 | polymlp | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Tl__MO_164437313211_000 | polymlp | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_V__MO_289723923467_000 | polymlp | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_W__MO_382353320753_000 | polymlp | Polynomial machine learning potential for W developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Y__MO_787548276025_000 | polymlp | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Zn__MO_013942547892_000 | polymlp | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Zr__MO_901319296850_000 | polymlp | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ag__MO_636501541840_000 | polymlp | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Al__MO_505948145953_000 | polymlp | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_As__MO_497481366479_000 | polymlp | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Au__MO_325811648164_000 | polymlp | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ba__MO_719111339457_000 | polymlp | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Be__MO_572696229114_000 | polymlp | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Bi__MO_155303177087_000 | polymlp | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Cr__MO_414457089579_000 | polymlp | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ge__MO_153960636203_000 | polymlp | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Hf__MO_302706142381_000 | polymlp | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ir__MO_955623544734_000 | polymlp | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_La__MO_670429137891_000 | polymlp | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Mo__MO_171399087989_000 | polymlp | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Nb__MO_039600711472_000 | polymlp | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Os__MO_303414393448_000 | polymlp | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_P__MO_096032629883_000 | polymlp | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Si__MO_525356107144_000 | polymlp | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Te__MO_530548424690_000 | polymlp | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ti__MO_856731010667_000 | polymlp | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ag__MO_935309189068_000 | polymlp | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Al__MO_741327671619_000 | polymlp | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_As__MO_277147085224_000 | polymlp | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Au__MO_308534295960_000 | polymlp | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ba__MO_754892597872_000 | polymlp | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Be__MO_938630133784_000 | polymlp | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Bi__MO_091780912029_000 | polymlp | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ca__MO_658935632136_000 | polymlp | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Cd__MO_201385456085_000 | polymlp | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Cr__MO_405944943357_000 | polymlp | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Cs__MO_516479295563_000 | polymlp | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Cu__MO_307870960219_000 | polymlp | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ga__MO_455323995017_000 | polymlp | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ge__MO_321247571012_000 | polymlp | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Hf__MO_767448300017_000 | polymlp | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Hg__MO_532512458775_000 | polymlp | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_In__MO_421845913188_000 | polymlp | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ir__MO_701278475153_000 | polymlp | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_K__MO_974844744880_000 | polymlp | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_La__MO_272407985288_000 | polymlp | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Li__MO_759936102551_000 | polymlp | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Mg__MO_111117764714_000 | polymlp | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Mo__MO_057634235224_000 | polymlp | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Na__MO_520429437472_000 | polymlp | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Nb__MO_054569351205_000 | polymlp | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Os__MO_175583695742_000 | polymlp | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_P__MO_350674028724_000 | polymlp | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Pb__MO_928219905404_000 | polymlp | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Pd__MO_372135877195_000 | polymlp | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Pt__MO_463206825133_000 | polymlp | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Rb__MO_996823884506_000 | polymlp | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Re__MO_677754121566_000 | polymlp | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Rh__MO_955089466881_000 | polymlp | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ru__MO_057203970637_000 | polymlp | Polynomial machine learning potential for Ru developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Sc__MO_402404331000_000 | polymlp | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Si__MO_584800695875_000 | polymlp | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Sn__MO_941232596453_000 | polymlp | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Sr__MO_500016842303_000 | polymlp | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ta__MO_221594740816_000 | polymlp | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Te__MO_119053663988_000 | polymlp | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ti__MO_498704797577_000 | polymlp | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Tl__MO_328290624408_000 | polymlp | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_V__MO_935781222742_000 | polymlp | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_W__MO_130162608338_000 | polymlp | Polynomial machine learning potential for W developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Y__MO_372004499101_000 | polymlp | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Zn__MO_915274476867_000 | polymlp | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Zr__MO_455180390661_000 | polymlp | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ag__MO_102756064984_000 | polymlp | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Al__MO_473810602608_000 | polymlp | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_As__MO_079527892021_000 | polymlp | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Au__MO_310892349221_000 | polymlp | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ba__MO_372263184744_000 | polymlp | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Be__MO_788262624433_000 | polymlp | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Cr__MO_019650365861_000 | polymlp | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ge__MO_205552143571_000 | polymlp | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Hf__MO_339993859628_000 | polymlp | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ir__MO_757141468606_000 | polymlp | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_La__MO_925272714883_000 | polymlp | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Mo__MO_618958452179_000 | polymlp | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Nb__MO_487415899293_000 | polymlp | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Os__MO_930178745031_000 | polymlp | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_P__MO_953154730976_000 | polymlp | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Si__MO_744893990044_000 | polymlp | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Te__MO_930850065494_000 | polymlp | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ti__MO_972411558592_000 | polymlp | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ag__MO_240369238264_000 | polymlp | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Al__MO_880067883971_000 | polymlp | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_As__MO_273539458220_000 | polymlp | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Au__MO_410001007835_000 | polymlp | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ba__MO_132467328236_000 | polymlp | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Be__MO_932695185972_000 | polymlp | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Bi__MO_629061695536_000 | polymlp | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ca__MO_629338023450_000 | polymlp | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Cd__MO_117089654786_000 | polymlp | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Cr__MO_737092069690_000 | polymlp | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Cs__MO_878584542829_000 | polymlp | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Cu__MO_142845792483_000 | polymlp | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ga__MO_154173534935_000 | polymlp | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ge__MO_469192742249_000 | polymlp | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Hf__MO_493705577112_000 | polymlp | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Hg__MO_504229735907_000 | polymlp | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_In__MO_256545325433_000 | polymlp | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ir__MO_146752984652_000 | polymlp | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_K__MO_138792914859_000 | polymlp | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_La__MO_318904455944_000 | polymlp | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Li__MO_666615010440_000 | polymlp | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Mg__MO_581222852664_000 | polymlp | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Mo__MO_214371386462_000 | polymlp | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Na__MO_503859980643_000 | polymlp | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Nb__MO_511596435272_000 | polymlp | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Os__MO_174802158248_000 | polymlp | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_P__MO_516340912478_000 | polymlp | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Pb__MO_298406547550_000 | polymlp | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Pd__MO_703884683416_000 | polymlp | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Pt__MO_562889622009_000 | polymlp | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Rb__MO_298971280178_000 | polymlp | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Re__MO_196791352634_000 | polymlp | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Rh__MO_440425426290_000 | polymlp | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ru__MO_402775474922_000 | polymlp | Polynomial machine learning potential for Ru developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Sc__MO_382670449972_000 | polymlp | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Si__MO_073458825522_000 | polymlp | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Sn__MO_824732483969_000 | polymlp | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Sr__MO_506937453247_000 | polymlp | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ta__MO_181313409373_000 | polymlp | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Te__MO_300992695616_000 | polymlp | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ti__MO_182502121600_000 | polymlp | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Tl__MO_164910812077_000 | polymlp | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_V__MO_087132030786_000 | polymlp | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_W__MO_146001374648_000 | polymlp | Polynomial machine learning potential for W developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Y__MO_263436104699_000 | polymlp | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Zn__MO_152270635677_000 | polymlp | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Zr__MO_158197888035_000 | polymlp | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ag__MO_341373946400_000 | polymlp | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Al__MO_559410074487_000 | polymlp | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_As__MO_961870608192_000 | polymlp | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Au__MO_779643048689_000 | polymlp | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ba__MO_157353590251_000 | polymlp | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Be__MO_515641496056_000 | polymlp | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Bi__MO_356657367492_000 | polymlp | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ca__MO_837602762416_000 | polymlp | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Cd__MO_350162857967_000 | polymlp | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Cr__MO_771039881137_000 | polymlp | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Cs__MO_839179562554_000 | polymlp | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Cu__MO_745561504899_000 | polymlp | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ga__MO_801474484441_000 | polymlp | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ge__MO_042012467922_000 | polymlp | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Hf__MO_023969363307_000 | polymlp | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Hg__MO_502986783854_000 | polymlp | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_In__MO_068363759658_000 | polymlp | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ir__MO_783343249128_000 | polymlp | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_K__MO_721514865519_000 | polymlp | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_La__MO_453035893746_000 | polymlp | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Li__MO_964367448849_000 | polymlp | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Mg__MO_455752709662_000 | polymlp | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Na__MO_456942459102_000 | polymlp | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Nb__MO_397309129886_000 | polymlp | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Os__MO_276661364825_000 | polymlp | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_P__MO_988417608661_000 | polymlp | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Pb__MO_660303636568_000 | polymlp | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Pd__MO_462323298833_000 | polymlp | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Pt__MO_971298066572_000 | polymlp | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Rb__MO_961004255907_000 | polymlp | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Re__MO_704581193415_000 | polymlp | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Rh__MO_653983873948_000 | polymlp | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Sc__MO_040886226556_000 | polymlp | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Si__MO_275365833597_000 | polymlp | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Sn__MO_053108889601_000 | polymlp | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Sr__MO_886791347263_000 | polymlp | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ta__MO_326652710145_000 | polymlp | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ti__MO_391511922130_000 | polymlp | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Tl__MO_558479896972_000 | polymlp | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_V__MO_512441689292_000 | polymlp | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_W__MO_391151058263_000 | polymlp | Polynomial machine learning potential for W developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Y__MO_230716576638_000 | polymlp | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Zn__MO_130747965596_000 | polymlp | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Zr__MO_724046521842_000 | polymlp | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ag__MO_341491917374_000 | polymlp | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Al__MO_246404761811_000 | polymlp | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Au__MO_374303863541_000 | polymlp | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ba__MO_559111961492_000 | polymlp | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Be__MO_067637039349_000 | polymlp | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Bi__MO_338888193464_000 | polymlp | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ca__MO_061286710401_000 | polymlp | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Cd__MO_564296947902_000 | polymlp | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Cr__MO_631295096534_000 | polymlp | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Cs__MO_202257793980_000 | polymlp | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Cu__MO_629261612896_000 | polymlp | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ga__MO_180554235366_000 | polymlp | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ge__MO_905396426131_000 | polymlp | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Hf__MO_091439942494_000 | polymlp | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Hg__MO_711742369362_000 | polymlp | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_In__MO_335295448533_000 | polymlp | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ir__MO_779046847081_000 | polymlp | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_K__MO_353040166006_000 | polymlp | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_La__MO_537536152313_000 | polymlp | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Li__MO_658935003268_000 | polymlp | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Mg__MO_457694844885_000 | polymlp | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Na__MO_774317753406_000 | polymlp | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Nb__MO_476137308171_000 | polymlp | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_P__MO_275217715948_000 | polymlp | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Pb__MO_656650588444_000 | polymlp | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Pd__MO_836748466719_000 | polymlp | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Pt__MO_820541957177_000 | polymlp | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Rb__MO_306208717766_000 | polymlp | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Re__MO_352844985070_000 | polymlp | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Rh__MO_391016194938_000 | polymlp | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Sc__MO_147759802608_000 | polymlp | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Si__MO_334116375414_000 | polymlp | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Sn__MO_973310469366_000 | polymlp | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Sr__MO_185342388623_000 | polymlp | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ta__MO_544931424312_000 | polymlp | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ti__MO_342885913756_000 | polymlp | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Tl__MO_594090581812_000 | polymlp | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_V__MO_291696688458_000 | polymlp | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Y__MO_026857916322_000 | polymlp | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Zn__MO_591828103224_000 | polymlp | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Zr__MO_304394077411_000 | polymlp | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_Polymorphic_BereSerra_2006_GaN__SM_518345582208_000 | polymorphic | LAMMPS Stillinger-Weber potential for the Ga-N system developed by Bere and Serra (2006) and implemented using the polymorphic framework of Zhou et al. (2015) v000 |
| Sim_LAMMPS_Polymorphic_NordAlbeErhart_2003_GaN__SM_333071728528_000 | polymorphic | LAMMPS BOP potential for the Ga-N system developed by Nord et al. (2003) and implemented using the polymorphic framework of Zhou et al. (2015) v000 |
| Sim_LAMMPS_Polymorphic_Zhou_2004_CuTa__SM_453737875254_000 | polymorphic | LAMMPS EAM potential for the Cu-Ta system developed by Zhou et al. (2004) and implemented using the polymorphic framework of Zhou et al. (2015) v000 |
| Sim_LAMMPS_Polymorphic_ZhouJonesChu_2017_GaInN__SM_887684855692_000 | polymorphic | LAMMPS Stillinger-Weber potential for the In-Ga-N system developed by Zhou, Jones and Chu (2017) and implemented using the polymorphic framework of Zhou et al. (2015) v000 |
| Model | Type | Title |
|---|---|---|
| ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si__MO_566683736730_000 | ppm | Three-body bond-order potential for Si by Purja Pun and Mishin (2017) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_ReaxFF_AnGoddard_2015_BC__SM_389039364091_000 | reax | LAMMPS ReaxFF potential for B4C developed by An and Goddard (2015) v000 |
| Sim_LAMMPS_ReaxFF_AryanpourVanDuinKubicki_2010_FeHO__SM_222964216001_001 | reax | LAMMPS ReaxFF potential for Fe-H-O systems developed by Aryanpour, van Duin, and Kubicki (2010) v001 |
| Sim_LAMMPS_ReaxFF_BroqvistKullgrenWolf_2015_CeO__SM_063950220736_000 | reax | LAMMPS ReaxFF potential for Ce-O systems developed by Broqvist et al. (2015) v000 |
| Sim_LAMMPS_ReaxFF_BrugnoliMiyataniAkaji_SiCeNaClHO_2023__SM_282799919035_000 | reax | LAMMPS ReaxFF potential for Ceria/Silica/Water/NaCl developed by Brugnoli et al. (2023) v000 |
| Sim_LAMMPS_ReaxFF_ChenowethVanDuinGoddard_2008_CHO__SM_584143153761_001 | reax | LAMMPS ReaxFF potential for hydrocarbon oxidation (C-H-O) developed by Chenoweth, van Duin, and Goddard (2008) v001 |
| Sim_LAMMPS_ReaxFF_ChenowethVanDuinPersson_2008_CHOV__SM_429148913211_001 | reax | LAMMPS ReaxFF potential for reactions between hydrocarbons and vanadium oxide clusters (C-H-O-V) developed by Chenoweth et al. (2008) v001 |
| Sim_LAMMPS_reaxFF_FthenakisPetsalakisTozzini_2022_CHON__SM_198543900691_000 | reax | LAMMPS ReaxFF potential for C-H-N-O systems developed by Fthenakis et al. (2022) v001 |
| Sim_LAMMPS_ReaxFF_IslamOstadhosseinBorodin_2015_LiS__SM_058492438145_000 | reax | LAMMPS ReaxFF potential for Li-S systems developed by Islam et al. (2014) v000 |
| Sim_LAMMPS_ReaxFF_KeithFantauzziJacob_2010_AuO__SM_974345878378_001 | reax | LAMMPS ReaxFF potential for Au-O systems developed by Keith et al. (2010) v001 |
| Sim_LAMMPS_ReaxFF_ManzanoMoeiniMarinelli_2012_CaSiOH__SM_714124634215_000 | reax | LAMMPS ReaxFF potential for Ca-Si-O-H systems developed by Manzano et al. (2012) v000 |
| Sim_LAMMPS_ReaxFF_PolsVincentLunaFilot_2021_CsPbI__SM_367523551183_000 | reax | LAMMPS ReaxFF potential for CsPbI3 developed by Pols et al (2021) v000 |
| Sim_LAMMPS_ReaxFF_RaymandVanDuinBaudin_2008_ZnOH__SM_449472104549_001 | reax | ReaxFF potential for Zn-O-H systems developed by Raymand et al. (2008) v001 |
| Sim_LAMMPS_ReaxFF_SinghSrinivasanNeekAmal_2013_CFH__SM_306840588959_000 | reax | LAMMPS ReaxFF potential for fluorographene (C-F-H) developed by Singh et al. (2013) v000 |
| Sim_LAMMPS_ReaxFF_StrachanVanDuinChakraborty_2003_CHNO__SM_107643900657_001 | reax | LAMMPS ReaxFF potential for RDX (C-H-N-O) systems developed by Strachan et al. (2003) v001 |
| Sim_LAMMPS_ReaxFF_WeismillerVanDuinLee_2010_BHNO__SM_327381922729_001 | reax | LAMMPS ReaxFF potential for Ammonia Borane (B-H-N-O) developed by Weismiller et al. (2010) v001 |
| Sim_LAMMPS_ReaxFF_XiaoShiHao_2017_PHOC__SM_424780295507_000 | reax | LAMMPS ReaxFF transferable potential for P/H/O/C systems with application to phosphorene developed by Xiao et al. (2017) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_SMTBQ_SallesPolitanoAmzallag_2016_Al__SM_404097633924_000 | smtbq | LAMMPS SMTBQ potential for Al developed by Salles et al. (2016) v000 |
| Sim_LAMMPS_SMTBQ_SallesPolitanoAmzallag_2016_AlO__SM_853967355976_000 | smtbq | LAMMPS SMTBQ potential for the Al-O system developed by Salles et al. (2016) v000 |
| Sim_LAMMPS_SMTBQ_SallesPolitanoAmzallag_2016_TiO__SM_349577644423_000 | smtbq | LAMMPS SMTBQ potential for the Ti-O system developed by Salles et al. (2016) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_SNAP_ChenDengTran_2017_Mo__SM_003882782678_000 | snap | LAMMPS SNAP potential for Mo developed by Chen et al. (2017) v000 |
| SNAP_ChenDengTran_2017_Mo__MO_698578166685_000 | snap | A spectral neighbor analysis potential for Mo developed by Chi Chen (2019) v000 |
| SNAP_LiChenZheng_2019_NbTaWMo__MO_560387080449_000 | snap | A spectral neighbor analysis potential for Nb-Mo-Ta-W developed by Xiangguo Li (2019) v000 |
| SNAP_LiHuChen_2018_Cu__MO_529419924683_000 | snap | A spectral neighbor analysis potential for Cu developed by Xiangguo Li (2019) v000 |
| SNAP_LiHuChen_2018_Ni__MO_913991514986_000 | snap | A spectral neighbor analysis potential for Ni developed by Xiangguo Li (2019) v000 |
| SNAP_LiHuChen_2018_NiMo__MO_468686727341_000 | snap | A spectral neighbor analysis potential for Ni-Mo developed by Xiangguo Li (2019) v000 |
| SNAP_ThompsonSwilerTrott_2015_Ta__MO_359768485367_000 | snap | Spectral Neighbor Analysis Potential (SNAP) for tantalum developed by Thompson, Swiler, Trott, et al. (2015) v000 |
| SNAP_WoodCusentinoWirth_2019_WBe__MO_939388497041_000 | snap | A spectral neighbor analysis potential for W-Be developed by Wood et al. (2019) v000 |
| SNAP_ZuoChenLi_2019_Cu__MO_931672895580_000 | snap | A spectral neighbor analysis potential for Cu developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Ge__MO_183216355174_000 | snap | A spectral neighbor analysis potential for Ge developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Li__MO_732106099012_000 | snap | A spectral neighbor analysis potential for Li developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Mo__MO_014123846623_000 | snap | A spectral neighbor analysis potential for Mo developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Ni__MO_365106510449_000 | snap | A spectral neighbor analysis potential for Ni developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Si__MO_869330304805_000 | snap | A spectral neighbor analysis potential for Si developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Cu__MO_265210066873_000 | snap | A quadratic spectral neighbor analysis potential for Cu developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Ge__MO_766484508139_000 | snap | A quadratic spectral neighbor analysis potential for Ge developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Li__MO_041269750353_000 | snap | A quadratic spectral neighbor analysis potential for Li developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Mo__MO_692442138123_000 | snap | A quadratic spectral neighbor analysis potential for Mo developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Ni__MO_263593395744_000 | snap | A quadratic spectral neighbor analysis potential for Ni developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Si__MO_721469752060_000 | snap | A quadratic spectral neighbor analysis potential for Si developed by Yunxing Zuo v000 |
| Model | Type | Title |
|---|---|---|
| ThreeBodyCluster_SRS_StephensonRadnySmith_1996_Si__MO_604248666067_000 | srs | Three-body cluster potential for Si by Stephenson, Radny and Smith (1996) v000 |
| Model | Type | Title |
|---|---|---|
| SW_BalamaneHaliciogluTiller_1992_Si__MO_113686039439_005 | sw | Stillinger-Weber potential for Si developed by Balamane, Halicioglu and Tiller (1992) v005 |
| SW_BalamaneHauchShi_2017Brittle_Si__MO_381114941873_003 | sw | Stillinger-Weber potential for brittle Si combining the modifications of Balamane et al. (1992) and Hauch et al. (1999) v003 |
| SW_BereSerra_2006_GaN__MO_861114678890_001 | sw | Stillinger-Weber potential for the Ga-N system developed by Bere and Serra (2006) v001 |
| SW_DingAndersen_1986_Ge__MO_775478537242_000 | sw | Stillinger-Weber potential for crystalline and amorphous Ge as well as germanene due to Ding and Andersen (1986) v000 |
| SW_HauchHollandMarder_1999Brittle_Si__MO_119167353542_005 | sw | Stillinger-Weber potential for brittle Si due to Hauch et al. (1999) v005 |
| SW_LeeHwang_2012GGA_Si__MO_040570764911_001 | sw | Stillinger-Weber potential for Si optimized for thermal conductivity due to Lee and Hwang (1985); GGA parameterization v001 |
| SW_LeeHwang_2012LDA_Si__MO_517338295712_001 | sw | Stillinger-Weber potential for Si optimized for thermal conductivity due to Lee and Hwang (1985); LDA parameterization v001 |
| SW_MX2_WenShirodkarPlechac_2017_MoS__MO_201919462778_001 | sw | Modified Stillinger-Weber potential (MX2) for monolayer MoS2 developed by Wen et al. (2017) v001 |
| SW_StillingerWeber_1985_Si__MO_405512056662_006 | sw | Stillinger-Weber potential for Si due to Stillinger and Weber (1985) v006 |
| SW_WangStroudMarkworth_1989_CdTe__MO_786496821446_001 | sw | Stillinger-Weber potential for the Cd-Te system developed by Wang, Stroud and Markworth (1989) v001 |
| SW_ZhangXieHu_2014OptimizedSW1_Si__MO_800412945727_005 | sw | Stillinger-Weber potential for Si optimized for silicene developed by Zhang et al. (2014); Parameterization 'Optimized SW1' v005 |
| SW_ZhangXieHu_2014OptimizedSW2_Si__MO_475612090600_005 | sw | Stillinger-Weber potential for Si optimized for silicene developed by Zhang et al. (2014); Parameterization 'Optimized SW2' v005 |
| SW_ZhouWardMartin_2013_CdTeZnSeHgS__MO_503261197030_003 | sw | Stillinger-Weber potential for the Zn-Cd-Hg-S-Se-Te system developed by Zhou et al. (2013) v003 |
| Model | Type | Title |
|---|---|---|
| SW_MX2_KurniawanPetrieWilliams_2021_MoS__MO_677328661525_000 | swmx2 | Modified Stillinger-Weber potential (MX2) for monolayer MoS2 by Kurniawan et al. (2022) v000 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_Table_GrogerVitekDlouhy_2020_CoCrFeMnNi__SM_786004631953_001 | table | LAMMPS tabular pair potential for the Co-Cr-Fe-Mn-Ni system developed by Groger, Vitek and Dlouhy (2020) v001 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_ExTeP_LosKroesAlbe_2017_BN__SM_692329995993_001 | tersoff | ExTeP potential for B-N developed by Los et al. (2017) v001 |
| Sim_LAMMPS_ModifiedTersoff_ByggmastarHodilleFerro_2018_BeO__SM_305223021383_000 | tersoff | LAMMPS Modified Tersoff potential for Be-O developed by Byggmästar et al. (2018) v000 |
| Sim_LAMMPS_ModifiedTersoff_KumagaiIzumiHara_2007_Si__SM_773333226968_000 | tersoff | LAMMPS Modified Tersoff potential for Si by Kumagai et al. (2007) v000 |
| Sim_LAMMPS_ModifiedTersoff_PurjaPunMishin_2017_Si__SM_184524061456_000 | tersoff | LAMMPS Modified Tersoff potential for Si developed by Purja Pun and Mishin (2017) v000 |
| Sim_LAMMPS_TersoffZBL_ByggmastarGranberg_2020_Fe__SM_958863895234_000 | tersoff | LAMMPS Tersoff-ZBL potential for Fe developed by J. Byggmästar and Granberg (2020) v000 |
| Sim_LAMMPS_TersoffZBL_DevanathanDiazdelaRubiaWeber_1998_SiC__SM_578912636995_000 | tersoff | LAMMPS Tersoff-ZBL potential for Si-C developed by Devanathan, Diaz de la Rubia, and Weber (1998) v000 |
| Sim_LAMMPS_TersoffZBL_HenrikssonBjorkasNordlund_2013_FeC__SM_473463498269_000 | tersoff | LAMMPS Tersoff-ZBL potential for Fe-C developed by Henriksson, Björkas and Nordlund (2013) v000 |
| Tersoff_LAMMPS_AlbeNordlundAverback_2002_PtC__MO_500121566391_004 | tersoff | Tersoff-style three-body potential for PtC developed by Albe, Nordlund, and Averback (2002) v004 |
| Tersoff_LAMMPS_AlbeNordlundNord_2002_GaAs__MO_799020228312_004 | tersoff | Tersoff-style three-body potential for GaAs developed by Albe et al. (2002) v004 |
| Tersoff_LAMMPS_ByggmastarNagelAlbe_2019_FeO__MO_608695023236_000 | tersoff | Tersoff-ZBL potential for FeO developed by Byggmastar et al. (2019) v000 |
| Tersoff_LAMMPS_DawLawsonBauschlicher_2011_HfB__MO_328263916986_000 | tersoff | Tersoff potential for hafnium diboride (HfB_2) developed by Daw et al. (2011) v000 |
| Tersoff_LAMMPS_DawLawsonBauschlicher_2011pot2_ZrB__MO_728716510644_000 | tersoff | Tersoff potential for zirconium diboride (ZrB2) developed by Daw et al. (2011) v000 |
| Tersoff_LAMMPS_ErhartAlbe_2005_SiC__MO_903987585848_005 | tersoff | Tersoff-style three-body potential for SiC developed by Erhart and Albe (2005) v005 |
| Tersoff_LAMMPS_ErhartAlbe_2005SiII_SiC__MO_408791041969_004 | tersoff | Tersoff-style three-body potential for SiC (with SiII parameter set) developed by Erhart and Albe (2005) v004 |
| Tersoff_LAMMPS_ErhartJuslinGoy_2006_ZnO__MO_616776018688_004 | tersoff | Tersoff-style three-body potential for ZnO developed by Erhart et al. (2006) v004 |
| Tersoff_LAMMPS_KinaciHaskinsSevik_2012_BNC__MO_105008013807_000 | tersoff | Tersoff-style three-body potential for the B-N-C system developed by Kinaci et al. (2012) v000 |
| Tersoff_LAMMPS_LindsayBroido_2010_C__MO_430669729256_000 | tersoff | Tersoff-style three-body potential for C modified by Lindsay (2010) v000 |
| Tersoff_LAMMPS_MahdizadehAkhlamadi_2017_Ge__MO_344019981553_000 | tersoff | Tersoff-style three-body potential for Ge developed by Mahdizadeh and Akhlamadi (2017) v000 |
| Tersoff_LAMMPS_MuellerErhartAlbe_2007_Fe__MO_137964310702_004 | tersoff | Tersoff-style three-body potential for bcc and fcc Fe developed by Müller, Erhart, and Albe (2007) v004 |
| Tersoff_LAMMPS_MunetohMotookaMoriguchi_2007_SiO__MO_501246546792_000 | tersoff | Tersoff-style three-body potential for SiO developed by Munetoh et al. (2007) v000 |
| Tersoff_LAMMPS_NordAlbeErhart_2003_GaN__MO_612061685362_004 | tersoff | Tersoff-style three-body potential for GaN developed by Nord et al. (2003) v004 |
| Tersoff_LAMMPS_PetismeGrenWahnstrom_2015_WCCo__MO_454528624659_000 | tersoff | Tersoff-style three-body potential for the W-C-Co system developed by Petisme, Gren, and Wahnstrom (2015) v000 |
| Tersoff_LAMMPS_PlummerRathodSrivastava_2021_TiAlC__MO_992900971352_000 | tersoff | Tersoff-style three-body potential for TiAlC developed by Plummer et al. (2021) v000 |
| Tersoff_LAMMPS_PlummerTucker_2019_TiAlC__MO_736419017411_000 | tersoff | Tersoff-style three-body potential for TiAlC developed by Plummer and Tucker (2019) v000 |
| Tersoff_LAMMPS_PlummerTucker_2019_TiSiC__MO_751442731010_000 | tersoff | Tersoff-style three-body potential for TiSiC developed by Plummer and Tucker (2019) v000 |
| Tersoff_LAMMPS_Tersoff_1988_C__MO_579868029681_004 | tersoff | Tersoff-style three-body potential for C developed by Tersoff (1988) v004 |
| Tersoff_LAMMPS_Tersoff_1988T2_Si__MO_245095684871_004 | tersoff | Tersoff T2 potential for silicon developed by Tersoff (1988) v004 |
| Tersoff_LAMMPS_Tersoff_1988T3_Si__MO_186459956893_004 | tersoff | Tersoff T3 potential for silicon developed by Tersoff (1988) v004 |
| Tersoff_LAMMPS_Tersoff_1989_SiC__MO_171585019474_004 | tersoff | Tersoff-style three-body potential for SiC developed by Tersoff (1989) v004 |
| Tersoff_LAMMPS_Tersoff_1989_SiGe__MO_350526375143_004 | tersoff | Tersoff-style three-body potential for SiGe developed by Tersoff (1989) v004 |
| Tersoff_LAMMPS_Tersoff_1990_SiC__MO_444207127575_000 | tersoff | Tersoff-style three-body potential for SiC developed by Tersoff (1990) v000 |
| Tersoff_LAMMPS_Tersoff_1994_SiC__MO_794973922560_000 | tersoff | Tersoff-style three-body potential for SiC developed by Tersoff (1994) v000 |
| Tersoff_LAMMPS_ZhangNguyen_2021_MoSe__MO_152208847456_001 | tersoff | Tersoff potentials for large deformation pathways and fracture of MoSe2 v001 |
| Model | Type | Title |
|---|---|---|
| TIDP_RajanWarnerCurtin_2016A_User01__MO_514760222899_001 | tidp | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model A, most ductile) v001 |
| TIDP_RajanWarnerCurtin_2016B_User01__MO_217710069583_001 | tidp | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model B) v001 |
| TIDP_RajanWarnerCurtin_2016C_User01__MO_072437275969_001 | tidp | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model C) v001 |
| TIDP_RajanWarnerCurtin_2016D_User01__MO_791486224463_001 | tidp | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model D) v001 |
| TIDP_RajanWarnerCurtin_2016E_User01__MO_971845881377_001 | tidp | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model E) v001 |
| TIDP_RajanWarnerCurtin_2016F_User01__MO_246297839798_001 | tidp | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model F, most brittle) v001 |
| Model | Type | Title |
|---|---|---|
| Dipole_Umeno_YSZ__MO_394669891912_001 | tsdipole | Dipole model potential optimized for YSZ (Yttria-stabilized zirconia) |
| Model | Type | Title |
|---|---|---|
| TT_Modified_HellmannBichVogel_2007_He__MO_126942667206_002 | tt | Ab initio ground state He+He Interaction potential developed by Hellmann et al. (2007) v002 |
| Model | Type | Title |
|---|---|---|
| Sim_LAMMPS_Vashishta_BranicioRinoGan_2009_InP__SM_090647175366_000 | vashishta | LAMMPS Vashishta potential for the In-P system developed by Branicio et al. (2009) v000 |
| Sim_LAMMPS_Vashishta_BroughtonMeliVashishta_1997_SiO__SM_422553794879_000 | vashishta | LAMMPS Vashishta potential for the Si-O system developed by Broughton et al. (1997) v000 |
| Sim_LAMMPS_Vashishta_NakanoKaliaVashishta_1994_SiO__SM_503555646986_000 | vashishta | LAMMPS Vashishta potential for the Si-O system developed by Nakano et al. (1994) v000 |
| Sim_LAMMPS_Vashishta_VashishtaKaliaNakano_2007_SiC__SM_196548226654_000 | vashishta | LAMMPS Vashishta potential for the Si-C system developed by Vashishta et al. (2007) v000 |
| Sim_LAMMPS_Vashishta_VashishtaKaliaRino_1990_SiO__SM_887826436433_000 | vashishta | LAMMPS Vashishta potential for the Si-O system developed by Vashishta et al. (1990) v000 |
| Model | Type | Title |
|---|---|---|
| ThreeBodyBondOrder_WR_WangRockett_1991_Si__MO_081872846741_000 | wr | Three-body bond-order potential for Si by Wang and Rockett (1991) v000 |