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 |
Simulator
"Any" means any KIM-compliant simulator, otherwise the model is a simulator model that only works with that specific simulator.
|
Title |
|---|---|---|
| DUNN_Gupta_2026N1_Si__MO_246333365926_000 | Any | Neural Network potential for Si by Gupta (2026) N=1 (in series of 3) v000 |
| DUNN_Gupta_2026N2_Si__MO_433917300633_000 | Any | Neural Network potential for Si by Gupta (2026) N=2 (in series of 3) v000 |
| DUNN_Gupta_2026N3_Si__MO_798099350634_000 | Any | Neural Network potential for Si by Gupta (2026) N=3 (in series of 3) v000 |
| DUNN_WenTadmor_2019v1_C__MO_584345505904_001 | Any | 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 | Any | 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 | Any | Dropout uncertainty neural network (DUNN) potential for condensed-matter carbon systems developed by Wen and Tadmor (2019) v001 |
| EAM_CubicNaturalSpline_AngeloMoodyBaskes_1995_Ni__MO_800536961967_004 | Any | 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 | Any | EAM potential (cubic natural spline tabulation) for Al developed by Ercolessi and Adams (1994) v003 |
| EAM_Dynamo_Ackland_1987_Au__MO_754413982908_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Ackland et al. (1987) v001 |
| EAM_Dynamo_Ackland_1992_Ti__MO_748534961139_006 | Any | 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 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for W developed by Ackland (2003) v006 |
| EAM_Dynamo_AcklandBaconCalder_1997_Fe__MO_142799717516_006 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Mo developed by Ackland and Thetford (1987) v000 |
| EAM_Dynamo_AcklandTichyVitek_1987_Ag__MO_212700056563_006 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ag developed by Ackland et al. (1987) v006 |
| EAM_Dynamo_AcklandTichyVitek_1987_Au__MO_104891429740_006 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Au due to Ackland et al. (1987) v006 |
| EAM_Dynamo_AcklandTichyVitek_1987_Cu__MO_179025990738_006 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Cu developed by Ackland et al. (1987) v006 |
| EAM_Dynamo_AcklandTichyVitek_1987_Ni__MO_977363131043_006 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ni developed by Ackland et al. (1987) v006 |
| EAM_Dynamo_AcklandTichyVitek_1987v2_Ag__MO_055919219575_001 | Any | 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 | Any | 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 | Any | 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 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Cu developed by Ackland and Vitek (1990) v001 |
| EAM_Dynamo_AcklandWoodingBacon_1995v2_Zr__MO_398441626455_001 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Be developed by Agrawal et al. (2013) v001 |
| EAM_Dynamo_AlleraRibeiroPerez_2022_FeC__MO_324606345076_000 | Any | 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 | Any | 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 | Any | EAM Potential for Fe, Ni, Cr, Pd, developed by Arora et al. (2021) v000 |
| EAM_Dynamo_BelandLuOsetskiy_2016_CoNi__MO_871937946490_001 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Chamati et al. (2006) v001 |
| EAM_Dynamo_ChenFangLiu_2019_WRe__MO_473237400492_000 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | EAM Finnis-Sinclair potential developed by Daramola et al. to model CrFeMnNi quaternary HEAs (2022) v000 |
| EAM_Dynamo_DeluigiPasianotValencia_2021_FeNiCrCoCu__MO_657255834688_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for FeNiCrCoCu developed by Deluigi et al. (2021) v001 |
| EAM_Dynamo_ErcolessiAdams_1994_Al__MO_123629422045_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Ercolessi and Adams (1994) v006 |
| EAM_Dynamo_FarkasCaro_2018_FeNiCrCoCu__MO_803527979660_001 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Nb developed by Fellinger, Park and Wilkins (2010) v006 |
| EAM_Dynamo_FischerSchmitzEich_2019_CuNi__MO_266134052596_001 | Any | EAM potential for Cu–Ni developed by Fischer et al. (2019) v001 |
| EAM_Dynamo_Foiles_1985_Cu__MO_831121933939_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Foiles (1985) for NiCu alloys v001 |
| EAM_Dynamo_Foiles_1985_Ni__MO_010059867259_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Foiles (1985) for NiCu alloys v001 |
| EAM_Dynamo_FoilesBaskesDaw_1986Universal3_Ag__MO_626948998302_001 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Foiles and Hoyt (2006) v001 |
| EAM_Dynamo_FortiniMendelevBuldyrev_2008_Ru__MO_114077951467_006 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Ru developed by Fortini et al. (2008) v006 |
| EAM_Dynamo_GolaPastewka_2018_CuAu__MO_426403318662_001 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Grochola et al. (2005) v001 |
| EAM_Dynamo_HaleWongZimmerman_2008PairHybrid_PdAgH__MO_104806802344_006 | Any | 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 | Any | 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 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for V developed by Han et al. (2003) v001 |
| EAM_Dynamo_HanZepedaAckland_2003_W__MO_286137913440_001 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for W developed by Han et al. (2003) v001 |
| EAM_Dynamo_HepburnAckland_2008_FeC__MO_143977152728_006 | Any | 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 | Any | 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 | Any | EMT potential (LAMMPS cubic hermite tabulation) for Al developed by Karsten, Norskov and Puska (1987) v001 |
| EAM_Dynamo_KumarLudhwaniDas_2023_FeH__MO_680566758384_000 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ta developed by Li et al. (2003) v006 |
| EAM_Dynamo_LiuAdams_1998_AlMg__MO_019873715786_001 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Liu, Ercolessi and Adams (2004) v001 |
| EAM_Dynamo_LiuLiuBorucki_1999_AlCu__MO_020851069572_001 | Any | 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 | Any | 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 | Any | EAM potential for the Nb-Ta-V system developed by Maresca and Curtin (2020) v001 |
| EAM_Dynamo_Marinica_2007_Fe__MO_466808877130_001 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Fe developed by Marinica (2007) v001 |
| EAM_Dynamo_Marinica_2011_Fe__MO_255315407910_001 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Fe developed by Marinica (2011) v001 |
| EAM_Dynamo_MarinicaVentelonGilbert_2013EAM2_W__MO_204305659515_001 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for W developed by Mason, Nguyen-Manh, Becquart (2017) v001 |
| EAM_Dynamo_Mendelev_2003_Fe__MO_546673549085_000 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Mendelev et al. (2003) v000 |
| EAM_Dynamo_Mendelev_2007_Zr__MO_848899341753_000 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Zr developed by Mendelev and Ackland (2007) v000 |
| EAM_Dynamo_Mendelev_2015_Na__MO_094065024556_001 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Na developed by Mendelev (2015) v001 |
| EAM_Dynamo_Mendelev_2018_Tb__MO_522239651961_001 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Tb developed by Mendelev (2018) v001 |
| EAM_Dynamo_MendelevAckland_2007_Zr__MO_537826574817_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Zr developed by Mendelev and Ackland (2007) v001 |
| EAM_Dynamo_MendelevAckland_2007v3_Zr__MO_004835508849_001 | Any | 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 | Any | 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 | Any | Finnis-Sinclair potential for the Fe-Ni-Cr system developed by Mendelev et al. (2020) v001 |
| EAM_Dynamo_MendelevFangYe_2015_AlSm__MO_338600200739_001 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Mendelev et al. (2003) v001 |
| EAM_Dynamo_MendelevHanSrolovitz_2003Potential2_Fe__MO_769582363439_006 | Any | 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 | Any | 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 | Any | 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 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Al developed by Mendelev et al. (2008) v006 |
| EAM_Dynamo_MendelevKramerBecker_2008_Cu__MO_945691923444_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Cu solidification developed by Mendelev et al. (2008) v006 |
| EAM_Dynamo_MendelevKramerHao_2012_Ni__MO_832600236922_006 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | Finnis-Sinclair potential for the Cu-Zr system developed by Mendelev et al. (2019) v001 |
| EAM_Dynamo_MendelevUnderwoodAckland_2016pot1_Ti__MO_143373446649_001 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for the Ni-Al system developed by Mishin (2004) v006 |
| EAM_Dynamo_MishinFarkasMehl_1999_Al__MO_651801486679_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Mishin et al. (1999) v006 |
| EAM_Dynamo_MishinFarkasMehl_1999_Ni__MO_400591584784_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Mishin et al. (1999) v006 |
| EAM_Dynamo_MishinMehlPapaconstantopoulos_2001_Cu__MO_346334655118_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Mishin, Mehl and Papaconstantopoulos (2001) v006 |
| EAM_Dynamo_MishinMehlPapaconstantopoulos_2002_NiAl__MO_109933561507_006 | Any | 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 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Na developed by Nichol and Ackland (2016) v001 |
| EAM_Dynamo_NicholAckland_2016v2_Cs__MO_144828415103_001 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Norman, Starikov and Stegailov (2012) v001 |
| EAM_Dynamo_OBrienBarrPrice_2018_PtAu__MO_946831081299_001 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Olsson (2009) v001 |
| EAM_Dynamo_Olsson_2009_V__MO_944449444863_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for V developed by Olsson (2009) v001 |
| EAM_Dynamo_Olsson_2009_W__MO_670013535154_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for W developed by Olsson (2009) v001 |
| EAM_Dynamo_Olsson_2010_Au__MO_228280943430_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Olsson (2010) v001 |
| EAM_Dynamo_OnatDurukanoglu_2014_CuNi__MO_592013496703_006 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Pun (2017) v001 |
| EAM_Dynamo_PunMishin_2009_NiAl__MO_751354403791_006 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for developed by Ravelo et al. (2013); Ta1 Interaction v001 |
| EAM_Dynamo_RaveloGermannGuerrero_2013Ta2_Ta__MO_330376344314_001 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential for Ca developed by Sheng et al. (2011) v001 |
| EAM_Dynamo_SmirnovaKuskinStarikov_2013_UMoXe__MO_679329885632_006 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for U developed by Smirnova, Starikov, and Stegailov (2012) v001 |
| EAM_Dynamo_SongMendelev_2021_AlSm__MO_722733117926_001 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Stoller et al. (2016) v001 |
| EAM_Dynamo_SturgeonLaird_2000_Al__MO_120808805541_006 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Mg developed by Sun et al. (2006) v006 |
| EAM_Dynamo_SunZhangMendelev_2022_Fe__MO_044341472608_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Sun et al. (2022) v001 |
| EAM_Dynamo_TehranchiCurtin_2010_NiH__MO_535504325462_004 | Any | 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 | Any | 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 | Any | EAM potential for Ag developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Al__MO_986439208975_001 | Any | EAM potential for Al developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Au__MO_355170963718_001 | Any | EAM potential for Au developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Cu__MO_808763161866_001 | Any | EAM potential for Cu developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Ni__MO_071073566434_001 | Any | EAM potential for Ni developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Pd__MO_150533546179_001 | Any | EAM potential for Pd developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_VoterChen_1993_Pt__MO_101785259970_001 | Any | EAM potential for Pt developed by Voter and Chen (1993) v001 |
| EAM_Dynamo_WangZhuXiang_2018pot2_Pb__MO_961101070310_001 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for the Fe-H system developed by Wen (2021) v001 |
| EAM_Dynamo_WilliamsMishinHamilton_2006_Ag__MO_131620013077_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ag developed by Williams, Mishin and Hamilton (2006) v006 |
| EAM_Dynamo_WilliamsMishinHamilton_2006_CuAg__MO_128703483589_006 | Any | 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 | Any | 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 | Any | Finnis-Sinclair potential (LAMMPS cubic hermite tabulation) for Mg developed by Wilson and Mendelev (2016) v001 |
| EAM_Dynamo_WineyKubotaGupta_2010_Al__MO_149316865608_006 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zhakhovsky et al. (2009) v001 |
| EAM_Dynamo_Zhakhovsky_2009_Au__MO_173248269481_002 | Any | EAM potential for gold in a wide range of compressions and temperatures v002 |
| EAM_Dynamo_Zhakhovsky_2026_Cu__MO_471966700555_000 | Any | EAM potential for Copper in a wide range of compressions and temperatures developed by Zhakhovsky (2026) v000 |
| EAM_Dynamo_ZhangAshcraftMendelev_2016_NiNb__MO_047308317761_001 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ag developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Al__MO_131650261510_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Au__MO_468407568810_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Au developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Co__MO_924630542818_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Co developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Cu__MO_127245782811_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_CuAgAuNiPdPtAlPbFeMoTaWMgCoTiZr__MO_870117231765_001 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Fe developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Mg__MO_137404467969_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Mg developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Mo__MO_271256517527_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Mo developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Ni__MO_110256178378_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ni developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Pb__MO_116920074573_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Pb developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Ta__MO_130046220009_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Ta developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_Ti__MO_723456820410_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Pb developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004_W__MO_524392058194_006 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Zr developed by Zhou, Johnson and Wadley (2004) v006 |
| EAM_Dynamo_ZhouJohnsonWadley_2004NISTretabulation_Ag__MO_505250810900_001 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zhou, Wadley and Johnson (2001) v001 |
| EAM_Dynamo_ZhouWadleyJohnson_2001_Cu__MO_380822813353_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Cu developed by Zhou, Wadley and Johnson (2001) v001 |
| EAM_Dynamo_ZhouWadleyJohnson_2001_Pt__MO_102190350384_006 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Pt developed by Zhou, Wadley and Johnson (2001) v006 |
| EAM_Dynamo_ZhouWadleyJohnson_2001_W__MO_621445647666_001 | Any | EAM potential (LAMMPS cubic hermite tabulation) for W developed by Zhou, Wadley and Johnson (2001) v001 |
| EAM_Dynamo_ZhouZimmermanWong_2008_PdH__MO_114797992931_001 | Any | 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 | Any | EAM potential (LAMMPS cubic hermite tabulation) for Al developed by Zope and Mishin (2003) v006 |
| EAM_Dynamo_ZopeMishin_2003_TiAl__MO_117656786760_006 | Any | 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 | Any | 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 | Any | EAM potential (IMD tabulation) for the Mg-Zn system developed by Brommer et al. (2009) v003 |
| EAM_IMD_BrommerGaehler_2006A_AlNiCo__MO_122703700223_003 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | EAM potential (magnetic, cubic tabulation) for Mo developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_Nb__MO_218026715338_002 | Any | EAM potential (magnetic, cubic tabulation) for Nb developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_Ta__MO_261274272789_002 | Any | EAM potential (magnetic, cubic tabulation) for Ta developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_V__MO_683890323730_002 | Any | EAM potential (magnetic, cubic tabulation) for V developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DerletNguyenDudarev_2007_W__MO_195478838873_002 | Any | EAM potential (magnetic, cubic tabulation) for W developed by Derlet, Nguyen-Manh and Dudarev (2007) v002 |
| EAM_MagneticCubic_DudarevDerlet_2005_Fe__MO_135034229282_002 | Any | EAM potential (magnetic, cubic tabulation) for magnetic Fe developed by Dudarev and Derlet (2005) v002 |
| EAM_MagneticCubic_MendelevHanSrolovitz_2003_Fe__MO_856295952425_002 | Any | EAM potential (cubic tabulation) for Fe developed by Mendelev et al. (2003) v002 |
| EAM_Mendelev_2019_CuZr__MO_945018740343_000 | Any | EAM potential for Cu-Zr developed by Mendelev (2019) v000 |
| EAM_NN_Johnson_1988_Cu__MO_887933271505_003 | Any | EAM Potential (analytical nearest-neighbor) for Cu developed by Johnson (1988) v003 |
| EAM_QuinticClampedSpline_ErcolessiAdams_1994_Al__MO_450093727396_003 | Any | EAM potential (clamped quintic tabulation) for Al developed by Ercolessi and Adams (1994) v003 |
| EAM_QuinticClampedSpline_Kim_2021_PtAu__MO_463728687265_001 | Any | 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 | Any | EAM potential (quintic hermite tabulation) for Al developed by Ercolessi and Adams (1994) v003 |
| EDIP_BelkoGusakovDorozhkin_2010_Ge__MO_129433059219_001 | Any | EDIP model for Ge developed by Belko, Gusakov and Dorozhkin (2010) v001 |
| EDIP_JustoBazantKaxiras_1998_Si__MO_958932894036_002 | Any | EDIP model for Si developed by Justo et al. (1998) v002 |
| EDIP_LAMMPS_JiangMorganSzlufarska_2012_SiC__MO_667792548433_000 | Any | EDIP model for SiC developed by Jiang, Morgan, and Szlufarska (2012) v000 |
| EDIP_LAMMPS_JustoBazantKaxiras_1998_Si__MO_315965276297_000 | Any | EDIP model for Si developed by Justo et al. (1998) v000 |
| EDIP_LAMMPS_LucasBertolusPizzagalli_2009_SiC__MO_634310164305_000 | Any | EDIP potential for Si-C developed by Lucas, Bertolus, and Pizzagalli (2009) v000 |
| EDIP_LAMMPS_Marks_2000_C__MO_374144505645_000 | Any | EDIP potential for C developed by Marks (2000) v000 |
| EMT_Asap_MetalGlass_BaileySchiotzJacobsen_2004_CuMg__MO_228059236215_001 | Any | EMT potential for Cu-Mg metallic glasses developed by Bailey, Schiotz, and Jacobsen (2004) v001 |
| EMT_Asap_MetalGlass_PaduraruKenoufiBailey_2007_CuZr__MO_987541074959_001 | Any | EMT potential for Cu-Zr metallic glasses developed by Paduraru et al. (2007) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Ag__MO_303974873468_001 | Any | EMT potential for Ag developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Al__MO_623376124862_001 | Any | EMT potential for Al developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_AlAgAuCuNiPdPt__MO_115316750986_001 | Any | 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 | Any | EMT potential for Au developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Cu__MO_396616545191_001 | Any | EMT potential for Cu developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Ni__MO_108408461881_001 | Any | EMT potential for Ni developed by Jacobsen, Stolze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Pd__MO_066802556726_001 | Any | EMT potential for Pd developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| EMT_Asap_Standard_JacobsenStoltzeNorskov_1996_Pt__MO_637493005914_001 | Any | EMT potential for Pt developed by Jacobsen, Stoltze, and Norskov (1996) v001 |
| Exp6_KongChakrabarty_1973_ArNe__MO_946046425752_002 | Any | Exp-6 pair potential for Ar-Ne with parameters due to Hogervorst and mixing rule due to Kong and Chakrabarty (1973) v002 |
| GEAM_LAMMPS_Samanta_2025_AlMgY__MO_534783049221_000 | Any | GEAM potential for the Al-Mg-Y system developed by Samanta (2025) v000 |
| GEAM_LAMMPS_Samanta_2025_MgY__MO_501769325268_000 | Any | GEAM potential for the Mg-Y system developed by Samanta (2025) v000 |
| GEAM_LAMMPS_ShiIyerSharma_2023_MoTaW__MO_785657481359_000 | Any | GEAM potential for the Mo-Ta-W system developed by Shi et al. (2023) v000 |
| GEAM_LAMMPS_ShiSamanta_2023_CrMoNbV__MO_397830378062_000 | Any | GEAM potential for the Cr-Mo-Nb-V system developed by Shi and Samanta (2023) v000 |
| GEAM_LAMMPS_ShiSamanta_2024_MoV__MO_337827773876_000 | Any | GEAM potential for the Mo-V system developed by Shi and Samanta (2024) v000 |
| GEAM_LAMMPS_WangSamanta_2026_LiTa__MO_118866552697_000 | Any | GEAM potential for the Li-Ta system developed by Wang and Samanta (2026) v000 |
| GEAM_LAMMPS_ZhaoSamanta_2024_FeNi__MO_361455675661_000 | Any | GEAM potential for the Fe-Ni system developed by Zhao and Samanta (2024) v000 |
| hNN_WenTadmor_2019Grx_C__MO_421038499185_001 | Any | A hybrid neural network potential for multilayer graphene systems developed by Wen and Tadmor (2019) v001 |
| LJ_ElliottAkerson_2015_Universal__MO_959249795837_003 | Any | 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 | Any | Lennard-Jones model (shifted) for Ar with parameters from Bernardes (1958) (high precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958HighCutoff_Kr__MO_923895531627_004 | Any | Lennard-Jones model (shfited) for Kr with parameters from Bernardes (1958) (high precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958HighCutoff_Ne__MO_966254629593_004 | Any | Lennard-Jones model (shifted) for Ne with parameters from Bernardes (1958) (high precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958HighCutoff_Xe__MO_796748253903_004 | Any | Lennard-Jones model (shifted) for Xe with parameters from Bernardes (1958) (high precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958LowCutoff_Ar__MO_720819638419_004 | Any | Lennard-Jones model (shifted) for Ar with parameters from Bernardes (1958) (low precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958LowCutoff_Kr__MO_995724792024_004 | Any | Lennard-Jones model (shifted) for Kr with parameters from Bernardes (1958) (low precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958LowCutoff_Ne__MO_466741694288_004 | Any | Lennard-Jones model (shifted) for Ne with parameters from Bernardes (1958) (low precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958LowCutoff_Xe__MO_648694198005_004 | Any | Lennard-Jones model (shifted) for Xe with parameters from Bernardes (1958) (low precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958MedCutoff_Ar__MO_126566794224_004 | Any | Lennard-Jones model (shifted) for Ar with parameters from Bernardes (1958) (medium precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958MedCutoff_Kr__MO_984281096460_004 | Any | Lennard-Jones model (shifted) for Kr with parameters from Bernardes (1958) (medium precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958MedCutoff_Ne__MO_160637895352_004 | Any | Lennard-Jones model (shifted) for Ne with parameters from Bernardes (1958) (medium precision cutoff) v004 |
| LJ_Shifted_Bernardes_1958MedCutoff_Xe__MO_849320763277_004 | Any | Lennard-Jones model (shifted) for Xe with parameters from Bernardes (1958) (medium precision cutoff) v004 |
| LJ_Smoothed_Bernardes_1958_Ar__MO_764178710049_001 | Any | Lennard-Jones potential (smoothed) for Ar with parameters from Bernardes (1958) v001 |
| LJ_Truncated_Nguyen_2005_Ar__MO_398194508715_001 | Any | Lennard-Jones potential (truncated) for Ar with parameters from Nguyen (2005) v001 |
| MEAM_LAMMPS_AgrawalMirzaeifar_2021_CuC__MO_028979335952_002 | Any | MEAM potential for Cu-C composites developed by Agrawal and Mirzaeifar (2021) v002 |
| MEAM_LAMMPS_AhmadGrohGhazisaeidi_2018_MgY__MO_135739722270_002 | Any | MEAM potential for Mg–Y alloys developed by Ahmad et al. (2018) v002 |
| MEAM_LAMMPS_AlmyrasSangiovanniSarakinos_2019_NAlTi__MO_958395190627_002 | Any | MEAM potential for the N-Al-Ti system developed by Almyras et al. v002 |
| MEAM_LAMMPS_AlviFaiyadMunshi_2022_AgAu__MO_511467482222_000 | Any | 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 | Any | MEAM potential for Cu developed by Asadi et al. (2015) v002 |
| MEAM_LAMMPS_AsadiZaeemNouranian_2015_Fe__MO_492310898779_002 | Any | MEAM potential for Fe developed by Asadi et al. (2015) v002 |
| MEAM_LAMMPS_AsadiZaeemNouranian_2015_Ni__MO_700541006254_002 | Any | MEAM potential for Ni developed by Asadi et al. (2015) v002 |
| MEAM_LAMMPS_AslamBaskesDickel_2019_FeMnSiC__MO_427873955970_002 | Any | MEAM Potential for the Fe-Mn-Si-C system developed by Aslam et al. (2019) v002 |
| MEAM_LAMMPS_Asthana_2026_GaMgN__MO_302603386351_000 | Any | MEAM Potential for the Ga–Mg–N Ternary System developed by Asthana (2026) v000 |
| MEAM_LAMMPS_ChoiJoSohn_2018_CoNiCrFeMn__MO_115454747503_002 | Any | 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 | Any | MEAM Potential for the Co-Cr system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_CoFe__MO_179158257180_002 | Any | MEAM Potential for the Co-Fe system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_CoMn__MO_808662295149_002 | Any | MEAM Potential for the Co-Mn system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_CrMn__MO_671124822359_002 | Any | MEAM Potential for the Cr-Mn system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_ChoiKimSeol_2017_NiMn__MO_348689608050_002 | Any | MEAM Potential for the Ni-Mn system developed by Choi et al. (2017) v002 |
| MEAM_LAMMPS_CostaAgrenClavaguera_2007_AlNi__MO_131642768288_002 | Any | MEAM Potential for the Al-Ni system developed by Silva et al. (2007) v002 |
| MEAM_LAMMPS_CuiGaoCui_2012_LiSi__MO_557492625287_002 | Any | MEAM potential for Li-Si alloys developed by Cui et al. (2012) v002 |
| MEAM_LAMMPS_DickelBaskesAslam_2018_MgAlZn__MO_093637366498_002 | Any | MEAM potential for Mg–Al–Zn alloys developed by Dickel et al. (2018) v002 |
| MEAM_LAMMPS_DongKimKo_2012_CoAl__MO_099716416216_002 | Any | MEAM Potential for the Co-Al system developed by Dong et al. (2012) v002 |
| MEAM_LAMMPS_DoShinLee_2008_In__MO_439532348190_001 | Any | MEAM Potential for In developed by Do, Shin and Lee (2008) v001 |
| MEAM_LAMMPS_DoShinLee_2009_GaInN__MO_815057898706_002 | Any | MEAM Potential for the Ga-In-N system developed by Do et al. (2009) v002 |
| MEAM_LAMMPS_DuLenoskyHennig_2011_Si__MO_883726743759_002 | Any | Spline-based MEAM potential for Si system developed by Du et al. (2011) v002 |
| MEAM_LAMMPS_EtesamiAsadi_2018_Cu__MO_227887284491_002 | Any | MEAM potential for Cu developed by Etesami and Asadi (2018) v002 |
| MEAM_LAMMPS_EtesamiAsadi_2018_Fe__MO_549900287421_002 | Any | MEAM potential for Fe developed by Etesami and Asadi (2018) v002 |
| MEAM_LAMMPS_EtesamiAsadi_2018_Ni__MO_937008984446_002 | Any | MEAM potential for Ni developed by Etesami and Asadi (2018) v002 |
| MEAM_LAMMPS_EtesamiBaskesLaradji_2018_PbSn__MO_162736908871_002 | Any | MEAM potential for Pb-Sn developed by Etesami et al. (2018) v002 |
| MEAM_LAMMPS_FernandezPascuet_2014_U__MO_399431830125_002 | Any | MEAM potential for U developed by Fernández and Pascuet (2014) v002 |
| MEAM_LAMMPS_FuemmelerVita_2023_Li__MO_386038428339_000 | Any | MEAM spline potential for Li developed by Fuemmeler and Vita (2023) v000 |
| MEAM_LAMMPS_GaoOteroAouadi_2013_AgTaO__MO_112077942578_002 | Any | MEAM potential for perovskite silver tantalate (AgTaO3) developed by Gao et al. (2013) v002 |
| MEAM_LAMMPS_HennigLenoskyTrinkle_2008_Ti__MO_520569947398_002 | Any | MEAM potential for Ti developed by Hennig et al. (2008) v002 |
| MEAM_LAMMPS_HiremathMelinBitzek_2022_W__MO_943864507178_001 | Any | MEAM Potential for W developed by Hiremath et al. (2022) v001 |
| MEAM_LAMMPS_HuangDongLiu_2018_Si__MO_050147023220_002 | Any | MEAM potential for Si developed by Huang et al. (2018) v002 |
| MEAM_LAMMPS_HuangLiuDuan_2021_HfNbTaTiZr__MO_893505888031_002 | Any | MEAM potential for HfNbTaTiZr alloy developed by Huang et al. (2021) v002 |
| MEAM_LAMMPS_JangKimLee_2018_ZnMg__MO_474962707676_002 | Any | MEAM Potential for the Mg-Zn system developed by Jang et al. (2018) v002 |
| MEAM_LAMMPS_JangSeolLee_2019_CaZnMg__MO_708495328010_002 | Any | MEAM Potential for the Ca-Zn-Mg system developed by Jang, Seol and Lee (2019) v002 |
| MEAM_LAMMPS_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__MO_262519520678_002 | Any | MEAM potential for Al-Si-Mg-Cu-Fe alloys developed by Jelinek et al. (2012) v002 |
| MEAM_LAMMPS_JeongLee_2020_PdC__MO_068985622065_002 | Any | MEAM Potential for the Pd-C system developed by Jeong, and Lee (2020) v002 |
| MEAM_LAMMPS_JeongLee_2020_PtC__MO_716623333967_002 | Any | MEAM Potential for the Pt-C system developed by Jeong, and Lee (2020) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdAl__MO_616482358807_002 | Any | MEAM Potential for the Pd-Al system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdCo__MO_101997554790_002 | Any | MEAM Potential for the Pd-Co system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdCu__MO_353393547686_002 | Any | MEAM Potential for the Pd-Cu system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdFe__MO_924736622203_002 | Any | MEAM Potential for the Pd-Fe system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdMo__MO_356501945107_002 | Any | MEAM Potential for the Pd-Mo system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdNi__MO_008996216289_002 | Any | MEAM Potential for the Pd-Ni system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_JeongParkDo_2018_PdTi__MO_086900950763_002 | Any | MEAM Potential for the Pd-Ti system developed by Jeong et al. (2018) v002 |
| MEAM_LAMMPS_KangEunJun_2014_SiC__MO_477506997611_002 | Any | MEAM Potential for the Si-C system developed by Kang et al. (2014) v002 |
| MEAM_LAMMPS_KangSaLee_2009_ZrAgCu__MO_813575892799_002 | Any | MEAM Potential for the Zr-Ag-Cu system developed by Kang et al. (2009) v002 |
| MEAM_LAMMPS_KavousiNovakBaskes_2019_NiTi__MO_050461957184_002 | Any | MEAM potential for Ni-Ti alloys developed by Kavousi et al, (2019) v002 |
| MEAM_LAMMPS_KimJeonLee_2015_MgCa__MO_611309973581_002 | Any | MEAM Potential for the Mg-Ca system developed by Kim, Jeon and Lee (2015) v002 |
| MEAM_LAMMPS_KimJeonLee_2015_MgSn__MO_935641703527_002 | Any | MEAM Potential for the Mg-Sn system developed by Kim, Jeon, and Lee (2015) v002 |
| MEAM_LAMMPS_KimJeonLee_2015_MgY__MO_018428823000_002 | Any | MEAM Potential for the Mg-Y system developed by Kim, Jeon, and Lee (2015) v002 |
| MEAM_LAMMPS_KimJungLee_2009_FeTiC__MO_110119204723_002 | Any | MEAM Potential for the Fe-Ti-C system developed by Kim, Jung, Lee (2009) v002 |
| MEAM_LAMMPS_KimJungLee_2010_FeNbC__MO_072689718616_002 | Any | MEAM Potential for the Fe-Nb-C system developed by Kim and Lee (2010) v002 |
| MEAM_LAMMPS_KimJungLee_2012_LiMg__MO_427397414195_002 | Any | MEAM Potential for the Li-Mg system developed by Kim, Jung, and Lee (2012) v002 |
| MEAM_LAMMPS_KimJungLee_2015_NiAlCo__MO_876687166519_002 | Any | MEAM Potential for the Ni-Al-Co system developed by Kim, Jung, and Lee, (2015) v002 |
| MEAM_LAMMPS_KimKimJung_2016_AlTi__MO_618133763375_002 | Any | MEAM Potential for the Al-Ti system developed by Kim et al. (2016) v002 |
| MEAM_LAMMPS_KimKimJung_2017_NiAlTi__MO_478967255435_002 | Any | MEAM Potential for the Ni-Al-Ti system developed by Kim et al. (2017) v002 |
| MEAM_LAMMPS_KimKimLee_2009_AlMg__MO_058537087384_002 | Any | MEAM Potential for the Al-Mg system developed by Kim, Kim, and Lee (2009) v002 |
| MEAM_LAMMPS_KimKoLee_2020_Na__MO_321355778754_002 | Any | MEAM Potential for the pure Na developed by Kim, Ko and Lee (2020) v002 |
| MEAM_LAMMPS_KimKoLee_2020_NaSn__MO_329881861557_002 | Any | MEAM Potential for the Na-Sn system developed by Kim, Ko and Shim (2020) v002 |
| MEAM_LAMMPS_KimLee_2006_PtFe__MO_343168101490_002 | Any | MEAM Potential for the Pt-Fe system developed by Kim, Koo, and Lee (2006) v002 |
| MEAM_LAMMPS_KimLee_2008_CuZr__MO_407917731909_001 | Any | MEAM Potential for the Cu-Zr system developed by Kim and Lee (2008) v001 |
| MEAM_LAMMPS_KimLee_2008_TiC__MO_134206624109_002 | Any | MEAM Potential for the Ti-C system developed by Kim, and Lee (2008) v002 |
| MEAM_LAMMPS_KimLee_2008_TiN__MO_070542625990_002 | Any | MEAM Potential for the Ti-N system developed by Kim and Lee (2008) v002 |
| MEAM_LAMMPS_KimLee_2017_MgNd__MO_059320827436_002 | Any | MEAM Potential for the Mg-Nd system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimLee_2017_MgPb__MO_325675357262_002 | Any | MEAM Potential for the Mg-Pb system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimLeeBaskes_2006_Ti__MO_472654156677_001 | Any | MEAM Potential for Ti developed by Kim, Lee, and Baskes (2006) v001 |
| MEAM_LAMMPS_KimLeeBaskes_2006_Zr__MO_392493010449_001 | Any | MEAM Potential for Zr developed by Kim, Lee, and Baskes (2006) v001 |
| MEAM_LAMMPS_KimSeolJi_2017_PtAl__MO_793141037706_002 | Any | MEAM Potential for the Pt-Al system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtCo__MO_545073984441_002 | Any | MEAM Potential for the Pt-Co system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtCu__MO_070797404269_002 | Any | MEAM Potential for the Pt-Cu system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtMo__MO_831380044253_002 | Any | MEAM Potential for the Pt-Mo system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtNi__MO_020840179467_002 | Any | MEAM Potential for the Pt-Ni system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtTi__MO_280985530673_002 | Any | MEAM Potential for the Pt-Ti system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimSeolJi_2017_PtV__MO_912978207512_002 | Any | MEAM Potential for the Pt-V system developed by Kim and Lee (2017) v002 |
| MEAM_LAMMPS_KimShinLee_2008_Ge__MO_657096500078_001 | Any | MEAM Potential for Ge developed by Kim, Shin and Lee (2008) v001 |
| MEAM_LAMMPS_KimShinLee_2009_FeMn__MO_058735400462_002 | Any | MEAM Potential for the Fe-Mn system developed by Kim, Shin, Lee (2009) v002 |
| MEAM_LAMMPS_KoGrabowskiNeugebauer_2015_NiTi__MO_663355627503_002 | Any | MEAM potential for Ni-Ti developed by Ko, Grabowski, and Neugebauer (2015) v002 |
| MEAM_LAMMPS_KoJimLee_2012_FeP__MO_179420363944_002 | Any | MEAM Potential for the Fe-P system developed by Ko, Kim, and Lee (2012) v002 |
| MEAM_LAMMPS_KoKimKwon_2018_Sn__MO_129364204512_002 | Any | MEAM potential for the pure tin (Sn) system developed by Ko et al. (2018) v002 |
| MEAM_LAMMPS_KoLee_2013_VPdY__MO_046547823135_002 | Any | MEAM Potential for the V-Pd-Y system developed by Ko and Lee (2013) v002 |
| MEAM_LAMMPS_KoShimLee_2011_AlH__MO_127847080751_002 | Any | MEAM Potential for the Al-H system developed by Ko, Shim, and Lee (2011) v002 |
| MEAM_LAMMPS_KoShimLee_2011_NiH__MO_091278480940_002 | Any | MEAM Potential for the Ni-H system developed by Ko et al. (2011) v002 |
| MEAM_LAMMPS_Lee_2006_FeC__MO_856956178669_002 | Any | MEAM Potential for the Fe-C system developed by Lee (2008) v002 |
| MEAM_LAMMPS_Lee_2007_Si__MO_774917820956_001 | Any | MEAM Potential for Si developed by Lee (2007) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Cr__MO_134550636109_001 | Any | MEAM Potential for Cr developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Fe__MO_196726067688_001 | Any | MEAM Potential for Fe developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Mo__MO_805823015127_001 | Any | MEAM Potential for Mo developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Nb__MO_802302521552_001 | Any | MEAM Potential for Nb developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_Ta__MO_644143102837_001 | Any | MEAM Potential for Ta developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_V__MO_868364924829_001 | Any | MEAM Potential for V developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeBaskesKim_2001_W__MO_227263111062_001 | Any | MEAM Potential for W developed by Lee et al. (2001) v001 |
| MEAM_LAMMPS_LeeJang_2007_FeH__MO_095610951957_002 | Any | MEAM Potential for the Fe-H system developed by Lee and Jang (2007) v002 |
| MEAM_LAMMPS_LeeLee_2005_C__MO_996970420049_001 | Any | MEAM Potential for C developed by Lee and Lee (2005) v001 |
| MEAM_LAMMPS_LeeLee_2010_FeAl__MO_332211522050_002 | Any | MEAM Potential for the Fe-Al system developed by Lee, and Lee. (2010) v002 |
| MEAM_LAMMPS_LeeLee_2014_ZrH__MO_946208788356_002 | Any | MEAM Potential for the Zr-H system developed by Lee and Lee (2014) v002 |
| MEAM_LAMMPS_LeeLeeKim_2006_FeN__MO_432861766738_002 | Any | MEAM Potential for the Fe-N system developed by Lee, Lee and Kim. (2006) v002 |
| MEAM_LAMMPS_LeeShim_2004_NiCu__MO_409065472403_002 | Any | MEAM Potential for the Ni-Cu system developed by Lee and Shim (2004) v002 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Ag__MO_969318541747_001 | Any | MEAM Potential for Ag developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Al__MO_353977746962_001 | Any | MEAM Potential for Al developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Au__MO_774911580446_001 | Any | MEAM Potential for Au developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Cu__MO_087820130586_001 | Any | MEAM Potential for Cu developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Ni__MO_000553624872_001 | Any | MEAM Potential for Ni developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Pb__MO_019208265157_001 | Any | MEAM Potential for Pb developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Pd__MO_307252285625_001 | Any | MEAM Potential for Pd developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimBaskes_2003_Pt__MO_534993486058_001 | Any | MEAM Potential for Pt developed by Lee, Shim, and Baskes (2003) v001 |
| MEAM_LAMMPS_LeeShimPark_2001_FeCr__MO_150993986463_001 | Any | MEAM Potential for the Fe-Cr system developed by Lee, Shim and Park (2001) v001 |
| MEAM_LAMMPS_LeeWirthShim_2005_FeCu__MO_063626065437_002 | Any | MEAM Potential for the Fe-Cu system developed by Lee et al. (2005) v002 |
| MEAM_LAMMPS_Lenosky_2017_W__MO_999198119251_002 | Any | MEAM Potential for W developed by Lenosky (2017) v002 |
| MEAM_LAMMPS_LenoskySadighAlonso_2000_Si__MO_533426548156_002 | Any | MEAM potential for Si system developed by Lenosky et al. (2000) v002 |
| MEAM_LAMMPS_LiyanageKimHouze_2014_FeC__MO_075279800195_002 | Any | MEAM potential for Fe-C developed by Liyanage et al. (2014) v002 |
| MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlFe__MO_304347095149_001 | Any | MEAM Potential for the Al-Fe system developed by Mahata, Mukhopadhyay and Asle Zaeem (2022) v001 |
| MEAM_LAMMPS_MahataMukhopadhyayAsleZaeem_2022_AlNi__MO_461927113651_001 | Any | MEAM Potential for the Al-Ni system developed by Mahata, Mukhopadhyay and Asle Zaeem (2022) v001 |
| MEAM_LAMMPS_MaiselKoZhang_2017_VNiTi__MO_744610363128_002 | Any | MEAM potential for V-Ni-Ti developed by Maisel et al. (2017) v002 |
| MEAM_LAMMPS_MirazDhariwalMeng_2020_CuNTi__MO_122936827583_002 | Any | MEAM potential for Ti/TiN and Cu/TiN interfaces developed by Miraz et al. (2020) v002 |
| MEAM_LAMMPS_MooreBeelerDeo_2015_UZr__MO_453094726678_001 | Any | MEAM potential for U-Zr alloy developed by Moore et al. (2015) v001 |
| MEAM_LAMMPS_MurallesParkKim_NiTi__MO_182729415169_000 | Any | MEAM potential for Ni-Ti developed by Muralles et al. (2017) v000 |
| MEAM_LAMMPS_NouranianTschoppGwaltney_2014_CH__MO_354152387712_002 | Any | MEAM potential for saturated hydrocarbons developed by Nouranian et al. (2014) v002 |
| MEAM_LAMMPS_OhSeolLee_2020_CoTi__MO_862371677648_002 | Any | MEAM Potential for the Co-Ti system developed by Oh, Seol, and Lee (2020) v002 |
| MEAM_LAMMPS_OhSeolLee_2020_CoV__MO_771146361182_002 | Any | MEAM Potential for the Co-V system developed by Oh, Seol, and Lee (2020) v002 |
| MEAM_LAMMPS_ParkFellingerLenosky_2012_Mo__MO_269937397263_002 | Any | MEAM Potential for Mo developed by Park et al. (2012) v002 |
| MEAM_LAMMPS_ParkFellingerLenosky_2012_Ta__MO_105449194206_002 | Any | MEAM Potential for Ta developed by Park et al. (2012) v002 |
| MEAM_LAMMPS_ParkFellingerLenosky_2012_W__MO_560940542741_002 | Any | MEAM Potential for W developed by Park et al. (2012) v002 |
| MEAM_LAMMPS_PascuetFernandez_2015_Al__MO_315820974149_002 | Any | MEAM potential for Al developed by Pascuet and Fernandez (2015) v002 |
| MEAM_LAMMPS_PascuetFernandez_2015_AlU__MO_596300673917_002 | Any | MEAM potential for Al-U developed by Pascuet and Fernandez (2015) v002 |
| MEAM_LAMMPS_RoyDuttaChakraborti_2021_AlLi__MO_971738391444_001 | Any | MEAM potential for Al and Al-Li alloys developed by Roy, Dutta, and Chakraborti (2021) v001 |
| MEAM_LAMMPS_SaLee_2008_FeTi__MO_260546967793_002 | Any | MEAM Potential for the Fe-Ti system developed by Sa and Lee (2008) v002 |
| MEAM_LAMMPS_SaLee_2008_NbFe__MO_162036141261_002 | Any | MEAM Potential for the Nb-Fe system developed by Sa and Lee (2008) v002 |
| MEAM_LAMMPS_SharifiWick_2025_FeMnNiTiCuCrCoAl__MO_675947402254_000 | Any | 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 | Any | MEAM Potential for the Al-V-H system developed by Shim et al. (2013) v002 |
| MEAM_LAMMPS_ShimKoKim_2013_NiVH__MO_612225165948_002 | Any | MEAM Potential for the Ni-V-H system developed by Shim et al. (2013) v002 |
| MEAM_LAMMPS_ShimLeeFleury_2011_VH__MO_072444764353_002 | Any | MEAM Potential for the V-H system developed by Shim et al. (2011) v002 |
| MEAM_LAMMPS_ShimParkCho_2003_NiW__MO_500937681860_002 | Any | MEAM Potential for the Ni-W system developed by Shim et al. (2003) v002 |
| MEAM_LAMMPS_SunRamachandranWick_2018_TiAl__MO_022920256108_002 | Any | MEAM potential for TiAl alloys developed by Sun et al. (2018) v002 |
| MEAM_LAMMPS_VellaChenStillinger_2017_Sn__MO_316045643888_002 | Any | MEAM potential for liquid Sn developed by Vella et al. (2017) v002 |
| MEAM_LAMMPS_Wagner_2007_Cu__MO_313717476091_002 | Any | MEAM potential for Cu developed by Wagner (2007) v002 |
| MEAM_LAMMPS_Wagner_2007_Ni__MO_444394830472_002 | Any | MEAM potential for Ni developed by Wagner (2007) v002 |
| MEAM_LAMMPS_Wagner_2007_SiC__MO_430846853065_002 | Any | MEAM potential for Si-C developed by Wagner (2007) v002 |
| MEAM_LAMMPS_WangOhLee_2020_CuCo__MO_694335101831_002 | Any | MEAM Potential for the Cu-Co system developed by Wang et al. (2020) v002 |
| MEAM_LAMMPS_WangOhLee_2020_CuCo__MO_849011491644_002 | Any | MEAM Potential for the Cu-Co system developed by Wang, Oh, and Lee (2020) v002 |
| MEAM_LAMMPS_WangOhLee_2020_CuMo__MO_380272712420_002 | Any | MEAM Potential for the Cu-Mo system developed by Wang, Oh, and Lee (2020) v002 |
| MEAM_LAMMPS_WangOhLee_2020_CuMo__MO_486450342170_002 | Any | MEAM Potential for the Cu-Mo system developed by Wang et al. (2020) v002 |
| MEAM_LAMMPS_WeiZhouLi_2019_BeO__MO_344044439515_002 | Any | MEAM potential for BeO structure developed by Wei et al. (2019) v002 |
| MEAM_LAMMPS_WuLeeSu_2017_NiCr__MO_880803040302_002 | Any | MEAM Potential for the Ni-Cr system developed by Wu, Lee, and Su (2017) v002 |
| MEAM_LAMMPS_WuLeeSu_2017_NiCrFe__MO_912636107108_002 | Any | MEAM Potential for the Ni-Cr-Fe system developed by Wu, Lee, and Su (2017) v002 |
| MEAM_LAMMPS_WuLeeSu_2017_NiFe__MO_321233176498_002 | Any | MEAM Potential for the Ni-Fe system developed by Wu, Lee, and Su (2017) v002 |
| MEAM_LAMMPS_YangQi_2019_Nb__MO_360068930164_002 | Any | MEAM potential for Niobium developed by Yang and Qi (2019) v002 |
| MEAM_LAMMPS_ZhangTrinkle_2016_TiO__MO_612732924171_002 | Any | MEAM potential for the Ti-O system developed by Zhang and Trinkle (2016) v002 |
| MEAM_LAMMPS_ZhouDickelBaskes_2021_Bi__MO_221877348962_001 | Any | MEAM Potential for Bi developed by Zhou et al. (2021) v001 |
| MFF_MistriotisFlytzanisFarantos_1989_Si__MO_080526771943_001 | Any | MFF potential for Si developed by Mistriotis, Flytzanis and Farantos (1989) v001 |
| MJ_MorrisAgaLevashov_2008_Fe__MO_857282754307_003 | Any | Modified Johnson pair potential for Fe developed by Morris, Aga, and Levashov (2008) v003 |
| Morse_EIP_GuthikondaElliott_2011_AuCd__MO_703849496106_002 | Any | 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 | Any | Morse potential (quintic smoothing) for Ar developed by Jelinek (1972) v002 |
| Morse_Shifted_GirifalcoWeizer_1959HighCutoff_Ag__MO_111986436268_004 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | 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 | Any | Morse potential (shifted) for Ne developed by Glyde (1970) v004 |
| Morse_Shifted_Jelinek_1972_Ar__MO_831902330215_004 | Any | Morse potential (shifted) for Ar by Jelinek (1972) v004 |
| Morse_SigmoidalSmoothed_Jelinek_1972_Ar__MO_071460865933_002 | Any | Morse potential (sigmoidal smoothing) for Ar developed by Jelinek (1972) v002 |
| PolyMLP_Seko_2022p1_AgAu__MO_823011623436_000 | Any | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgBa__MO_276055717124_000 | Any | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgIn__MO_034422676833_000 | Any | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgLa__MO_772792233950_000 | Any | Polynomial machine learning potential for Ag-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgPb__MO_648223078455_000 | Any | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AgSn__MO_101110678269_000 | Any | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlAg__MO_350112129763_000 | Any | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlAu__MO_773361983100_000 | Any | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlBa__MO_555884071866_000 | Any | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlCa__MO_876726883487_000 | Any | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlCu__MO_665550932377_000 | Any | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlGa__MO_483523650549_000 | Any | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlGe__MO_727636380924_000 | Any | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlIn__MO_713803919996_000 | Any | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlK__MO_735281460727_000 | Any | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlLa__MO_996217515025_000 | Any | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlNb__MO_335935516233_000 | Any | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlPb__MO_560806804746_000 | Any | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlPt__MO_028787619252_000 | Any | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlSc__MO_249690680481_000 | Any | Polynomial machine learning potential for Al-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlSi__MO_772463641678_000 | Any | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlSn__MO_036654852882_000 | Any | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlSr__MO_948606927949_000 | Any | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlV__MO_065088101597_000 | Any | Polynomial machine learning potential for Al-V developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlY__MO_936457231816_000 | Any | Polynomial machine learning potential for Al-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlZn__MO_683258743000_000 | Any | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_AlZr__MO_937296258990_000 | Any | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_BeAl__MO_847694853332_000 | Any | Polynomial machine learning potential for Be-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_BeGe__MO_396933178400_000 | Any | Polynomial machine learning potential for Be-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_BeSi__MO_788283766723_000 | Any | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_BeSn__MO_314058940376_000 | Any | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaAg__MO_595358060477_000 | Any | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaAu__MO_732840578400_000 | Any | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaCu__MO_017053542787_000 | Any | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaGa__MO_005649812247_000 | Any | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaGe__MO_803284128474_000 | Any | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaIn__MO_267537996802_000 | Any | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaLa__MO_879970448875_000 | Any | Polynomial machine learning potential for Ca-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaPb__MO_943415111691_000 | Any | Polynomial machine learning potential for Ca-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaSc__MO_367705290077_000 | Any | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaSn__MO_351332362877_000 | Any | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaTi__MO_149740904039_000 | Any | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CaZn__MO_840339866631_000 | Any | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CuAg__MO_537857072278_000 | Any | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CuAu__MO_823062543360_000 | Any | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_CuZn__MO_795242620384_000 | Any | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GaAg__MO_212356614349_000 | Any | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GaSn__MO_067490656712_000 | Any | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GeIn__MO_567938768570_000 | Any | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GePb__MO_391827440225_000 | Any | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GeSn__MO_544175726222_000 | Any | Polynomial machine learning potential for Ge-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_GeSr__MO_054998251352_000 | Any | Polynomial machine learning potential for Ge-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KAg__MO_199727096976_000 | Any | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KAu__MO_356560124445_000 | Any | Polynomial machine learning potential for K-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KCu__MO_255099340995_000 | Any | Polynomial machine learning potential for K-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KGe__MO_459655813835_000 | Any | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KIn__MO_924892044915_000 | Any | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KPb__MO_720100563535_000 | Any | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KSn__MO_076821718242_000 | Any | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_KZn__MO_321402413208_000 | Any | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiAg__MO_001132182851_000 | Any | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiAl__MO_080005454372_000 | Any | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiAu__MO_040937774580_000 | Any | Polynomial machine learning potential for Li-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiBa__MO_773440616906_000 | Any | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiBe__MO_981139059532_000 | Any | Polynomial machine learning potential for Li-Be developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiBi__MO_112699923754_000 | Any | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiCa__MO_384169544942_000 | Any | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiCu__MO_053552052173_000 | Any | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiGa__MO_483161604769_000 | Any | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiGe__MO_911566550468_000 | Any | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiIn__MO_761614630215_000 | Any | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiMg__MO_199365257743_000 | Any | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiPb__MO_466687827018_000 | Any | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiSi__MO_246471043381_000 | Any | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiSn__MO_081059051967_000 | Any | Polynomial machine learning potential for Li-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiSr__MO_554122036474_000 | Any | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiTi__MO_671224571016_000 | Any | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_LiZn__MO_783498026982_000 | Any | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgAg__MO_643231797558_000 | Any | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgAl__MO_670946385478_000 | Any | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgAu__MO_142560144114_000 | Any | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgCa__MO_874169223165_000 | Any | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgCu__MO_683438982122_000 | Any | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgGa__MO_549948643305_000 | Any | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgGe__MO_534496340093_000 | Any | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgIn__MO_723240239962_000 | Any | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgK__MO_994423577393_000 | Any | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgSc__MO_471453066610_000 | Any | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgSi__MO_764015570853_000 | Any | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgSn__MO_110075421146_000 | Any | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgTi__MO_989121378877_000 | Any | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_MgY__MO_471276375465_000 | Any | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaAl__MO_354202555176_000 | Any | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaGa__MO_298444110548_000 | Any | Polynomial machine learning potential for Na-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaGe__MO_547006368556_000 | Any | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaIn__MO_587982479968_000 | Any | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaMg__MO_586851152483_000 | Any | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaPb__MO_543960217468_000 | Any | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaSi__MO_512180175980_000 | Any | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaSn__MO_294901016081_000 | Any | Polynomial machine learning potential for Na-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_NaSr__MO_364777300293_000 | Any | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_PdPt__MO_064180783338_000 | Any | Polynomial machine learning potential for Pd-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiAg__MO_558818721890_000 | Any | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiAu__MO_033687705195_000 | Any | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiCu__MO_009960336295_000 | Any | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiGa__MO_959459596539_000 | Any | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiGe__MO_462952553615_000 | Any | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiIn__MO_197188842574_000 | Any | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiPb__MO_963333486681_000 | Any | Polynomial machine learning potential for Si-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiSn__MO_118151700862_000 | Any | Polynomial machine learning potential for Si-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SiZn__MO_902337818624_000 | Any | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SnPb__MO_340633106548_000 | Any | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_SrPb__MO_381597336914_000 | Any | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_TiAl__MO_223894196126_000 | Any | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_TiGe__MO_025114491917_000 | Any | Polynomial machine learning potential for Ti-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnAg__MO_002780648259_000 | Any | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnAu__MO_656935464156_000 | Any | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnGa__MO_203911770964_000 | Any | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnGe__MO_072257033094_000 | Any | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnIn__MO_258915657943_000 | Any | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p1_ZnSn__MO_516198383864_000 | Any | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgAu__MO_944402137439_000 | Any | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgBa__MO_857123287577_000 | Any | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgIn__MO_702096149594_000 | Any | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgLa__MO_409439888675_000 | Any | Polynomial machine learning potential for Ag-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgPb__MO_595083573723_000 | Any | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AgSn__MO_210942986619_000 | Any | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlAg__MO_540932853733_000 | Any | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlAu__MO_200707653568_000 | Any | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlBa__MO_215780095507_000 | Any | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlCa__MO_887341599634_000 | Any | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlCu__MO_933443908371_000 | Any | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlGa__MO_031943987658_000 | Any | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlGe__MO_788236841133_000 | Any | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlIn__MO_750879363282_000 | Any | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlK__MO_359877761911_000 | Any | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlLa__MO_031192979633_000 | Any | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlNb__MO_344619612839_000 | Any | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlPb__MO_640728266160_000 | Any | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlPt__MO_114321754774_000 | Any | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlSc__MO_975921301943_000 | Any | Polynomial machine learning potential for Al-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlSi__MO_795457664825_000 | Any | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlSn__MO_726394842236_000 | Any | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlSr__MO_233853181609_000 | Any | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlV__MO_021520966414_000 | Any | Polynomial machine learning potential for Al-V developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlY__MO_605437015539_000 | Any | Polynomial machine learning potential for Al-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlZn__MO_194371430688_000 | Any | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_AlZr__MO_403991319854_000 | Any | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_BeAl__MO_321074169214_000 | Any | Polynomial machine learning potential for Be-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_BeGe__MO_817754113288_000 | Any | Polynomial machine learning potential for Be-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_BeSi__MO_262355795611_000 | Any | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_BeSn__MO_649082660934_000 | Any | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaAg__MO_808961412194_000 | Any | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaAu__MO_597464483809_000 | Any | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaCu__MO_274542225214_000 | Any | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaGa__MO_576362609785_000 | Any | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaGe__MO_751203544778_000 | Any | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaIn__MO_929722546386_000 | Any | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaLa__MO_112910335336_000 | Any | Polynomial machine learning potential for Ca-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaPb__MO_233509421447_000 | Any | Polynomial machine learning potential for Ca-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaSc__MO_965847074706_000 | Any | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaSn__MO_313708305794_000 | Any | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaTi__MO_183166784696_000 | Any | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CaZn__MO_222201343732_000 | Any | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CuAg__MO_495090193440_000 | Any | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CuAu__MO_864476668504_000 | Any | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_CuZn__MO_352663723076_000 | Any | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GaAg__MO_246275243138_000 | Any | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GaSn__MO_673995264882_000 | Any | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GeIn__MO_884720655346_000 | Any | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GePb__MO_675520716098_000 | Any | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GeSn__MO_877602326315_000 | Any | Polynomial machine learning potential for Ge-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_GeSr__MO_777727202802_000 | Any | Polynomial machine learning potential for Ge-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KAg__MO_875335959983_000 | Any | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KAu__MO_929949809109_000 | Any | Polynomial machine learning potential for K-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KCu__MO_192585302435_000 | Any | Polynomial machine learning potential for K-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KGe__MO_722132851668_000 | Any | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KIn__MO_362211213503_000 | Any | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KPb__MO_806670238256_000 | Any | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KSn__MO_952660254314_000 | Any | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_KZn__MO_027391677821_000 | Any | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiAg__MO_185411762954_000 | Any | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiAl__MO_960827550644_000 | Any | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiAu__MO_940868595740_000 | Any | Polynomial machine learning potential for Li-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiBa__MO_286683749335_000 | Any | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiBe__MO_157152668775_000 | Any | Polynomial machine learning potential for Li-Be developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiBi__MO_314321973529_000 | Any | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiCa__MO_941364692155_000 | Any | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiCu__MO_071714769833_000 | Any | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiGa__MO_496584090412_000 | Any | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiGe__MO_760725313082_000 | Any | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiIn__MO_876031080716_000 | Any | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiMg__MO_588232939920_000 | Any | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiPb__MO_621271114150_000 | Any | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiSi__MO_119176603929_000 | Any | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiSn__MO_572938093835_000 | Any | Polynomial machine learning potential for Li-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiSr__MO_781397296555_000 | Any | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiTi__MO_995825976587_000 | Any | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_LiZn__MO_574727852694_000 | Any | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgAg__MO_350904051925_000 | Any | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgAl__MO_644896580510_000 | Any | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgAu__MO_594489219664_000 | Any | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgCa__MO_204288236461_000 | Any | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgCu__MO_776403086657_000 | Any | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgGa__MO_267258848031_000 | Any | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgGe__MO_559191134710_000 | Any | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgIn__MO_319608573159_000 | Any | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgK__MO_244539768462_000 | Any | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgSc__MO_532956650898_000 | Any | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgSi__MO_713701275105_000 | Any | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgSn__MO_827102424057_000 | Any | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgTi__MO_216584073599_000 | Any | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_MgY__MO_181738427501_000 | Any | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaAl__MO_672850893186_000 | Any | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaGa__MO_994323063865_000 | Any | Polynomial machine learning potential for Na-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaGe__MO_332071011861_000 | Any | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaIn__MO_145634060849_000 | Any | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaMg__MO_416243651908_000 | Any | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaPb__MO_069280874556_000 | Any | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaSi__MO_021552775560_000 | Any | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaSn__MO_422482847539_000 | Any | Polynomial machine learning potential for Na-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_NaSr__MO_337323093842_000 | Any | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_PdPt__MO_521848844432_000 | Any | Polynomial machine learning potential for Pd-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiAg__MO_132921485586_000 | Any | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiAu__MO_885747768449_000 | Any | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiCu__MO_103337519975_000 | Any | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiGa__MO_729188142180_000 | Any | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiGe__MO_816965513628_000 | Any | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiIn__MO_916358924324_000 | Any | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiPb__MO_990976689374_000 | Any | Polynomial machine learning potential for Si-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiSn__MO_367791721365_000 | Any | Polynomial machine learning potential for Si-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SiZn__MO_981137873400_000 | Any | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SnPb__MO_044027005325_000 | Any | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_SrPb__MO_573083564162_000 | Any | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_TiAl__MO_578636983448_000 | Any | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_TiGe__MO_799222074320_000 | Any | Polynomial machine learning potential for Ti-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnAg__MO_305154668187_000 | Any | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnAu__MO_767971944157_000 | Any | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnGa__MO_724449532698_000 | Any | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnGe__MO_936651575259_000 | Any | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnIn__MO_566847094064_000 | Any | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p2_ZnSn__MO_543973400203_000 | Any | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgAu__MO_379712378791_000 | Any | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgBa__MO_507856124860_000 | Any | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgIn__MO_098332856981_000 | Any | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgLa__MO_451410538946_000 | Any | Polynomial machine learning potential for Ag-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgPb__MO_028613465923_000 | Any | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AgSn__MO_389004779206_000 | Any | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlAg__MO_773586197956_000 | Any | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlAu__MO_044790454312_000 | Any | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlBa__MO_079128391049_000 | Any | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlCa__MO_263974011241_000 | Any | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlCu__MO_133987906701_000 | Any | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlGa__MO_679544221140_000 | Any | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlGe__MO_658352597054_000 | Any | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlIn__MO_069707644416_000 | Any | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlK__MO_508800702371_000 | Any | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlLa__MO_309404032314_000 | Any | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlNb__MO_469837818614_000 | Any | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlPb__MO_979853256057_000 | Any | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlPt__MO_825557510061_000 | Any | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlSc__MO_502197185646_000 | Any | Polynomial machine learning potential for Al-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlSi__MO_466510930009_000 | Any | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlSn__MO_609660235135_000 | Any | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlSr__MO_681566610227_000 | Any | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlY__MO_531083567860_000 | Any | Polynomial machine learning potential for Al-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlZn__MO_535776581463_000 | Any | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_AlZr__MO_492687405328_000 | Any | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_BeAl__MO_846123031688_000 | Any | Polynomial machine learning potential for Be-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_BeGe__MO_521014392699_000 | Any | Polynomial machine learning potential for Be-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_BeSi__MO_255226158073_000 | Any | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_BeSn__MO_951792558467_000 | Any | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaAg__MO_724078251814_000 | Any | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaAu__MO_967921588432_000 | Any | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaCu__MO_380370949024_000 | Any | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaGa__MO_322998972979_000 | Any | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaGe__MO_997859060795_000 | Any | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaIn__MO_365720691370_000 | Any | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaLa__MO_082791912842_000 | Any | Polynomial machine learning potential for Ca-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaPb__MO_887856246185_000 | Any | Polynomial machine learning potential for Ca-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaSc__MO_556957522634_000 | Any | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaSn__MO_801669282358_000 | Any | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaTi__MO_237846126333_000 | Any | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CaZn__MO_572892941444_000 | Any | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CuAg__MO_344462201016_000 | Any | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CuAu__MO_716806330835_000 | Any | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_CuZn__MO_302853281913_000 | Any | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GaAg__MO_577752152732_000 | Any | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GaSn__MO_639698572776_000 | Any | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GeIn__MO_640570535503_000 | Any | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GePb__MO_131114616619_000 | Any | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_GeSn__MO_912745532992_000 | Any | Polynomial machine learning potential for Ge-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KAg__MO_886262818219_000 | Any | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KAu__MO_086478926031_000 | Any | Polynomial machine learning potential for K-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KCu__MO_169026024582_000 | Any | Polynomial machine learning potential for K-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KGe__MO_555348570218_000 | Any | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KIn__MO_719208417667_000 | Any | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KPb__MO_827816257398_000 | Any | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KSn__MO_490581159197_000 | Any | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_KZn__MO_217149617540_000 | Any | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiAg__MO_302731467895_000 | Any | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiAl__MO_056718076690_000 | Any | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiAu__MO_072929907452_000 | Any | Polynomial machine learning potential for Li-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiBa__MO_522523401676_000 | Any | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiBe__MO_007700728408_000 | Any | Polynomial machine learning potential for Li-Be developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiBi__MO_058150661474_000 | Any | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiCa__MO_127854321970_000 | Any | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiCu__MO_161493229607_000 | Any | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiGa__MO_911736184845_000 | Any | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiGe__MO_602908061110_000 | Any | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiIn__MO_366749846814_000 | Any | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiMg__MO_837858250320_000 | Any | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiPb__MO_300447529744_000 | Any | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiSi__MO_538467579588_000 | Any | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiSn__MO_291911517283_000 | Any | Polynomial machine learning potential for Li-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiSr__MO_240370039202_000 | Any | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiTi__MO_971492312043_000 | Any | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_LiZn__MO_776440448715_000 | Any | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgAg__MO_964339722169_000 | Any | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgAl__MO_744396195472_000 | Any | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgAu__MO_948981209922_000 | Any | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgCa__MO_483959833126_000 | Any | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgCu__MO_719412165638_000 | Any | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgGa__MO_503838648358_000 | Any | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgGe__MO_108494656671_000 | Any | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgIn__MO_028623231466_000 | Any | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgK__MO_510807505506_000 | Any | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgSc__MO_340958950771_000 | Any | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgSi__MO_812147479425_000 | Any | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgSn__MO_254649659157_000 | Any | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgTi__MO_727052546981_000 | Any | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_MgY__MO_950072061157_000 | Any | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaAl__MO_679720458023_000 | Any | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaGa__MO_506336950147_000 | Any | Polynomial machine learning potential for Na-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaGe__MO_404910834284_000 | Any | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaIn__MO_390744351074_000 | Any | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaMg__MO_535937059130_000 | Any | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaPb__MO_964178395930_000 | Any | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaSi__MO_801816315004_000 | Any | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaSn__MO_507154403588_000 | Any | Polynomial machine learning potential for Na-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_NaSr__MO_036658387001_000 | Any | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_PdPt__MO_397668178553_000 | Any | Polynomial machine learning potential for Pd-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiAg__MO_917105717444_000 | Any | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiAu__MO_173801784889_000 | Any | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiCu__MO_393479569734_000 | Any | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiGa__MO_198498705046_000 | Any | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiGe__MO_699353865120_000 | Any | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiIn__MO_812549533556_000 | Any | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiPb__MO_528321941482_000 | Any | Polynomial machine learning potential for Si-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiSn__MO_009807584020_000 | Any | Polynomial machine learning potential for Si-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SiZn__MO_030993907682_000 | Any | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SnPb__MO_536989370303_000 | Any | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_SrPb__MO_824270967893_000 | Any | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_TiAl__MO_945531783513_000 | Any | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_TiGe__MO_607202239115_000 | Any | Polynomial machine learning potential for Ti-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnAg__MO_283932177215_000 | Any | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnAu__MO_613106811566_000 | Any | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnGa__MO_605652434522_000 | Any | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnGe__MO_943988361552_000 | Any | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnIn__MO_935571150853_000 | Any | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p3_ZnSn__MO_361197325697_000 | Any | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgAu__MO_920701415341_000 | Any | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgBa__MO_194579513066_000 | Any | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgIn__MO_266812879859_000 | Any | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgPb__MO_060209960188_000 | Any | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AgSn__MO_551463947779_000 | Any | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlAg__MO_575237573187_000 | Any | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlAu__MO_957328769065_000 | Any | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlBa__MO_197659268820_000 | Any | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlCa__MO_658245269236_000 | Any | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlCu__MO_683961731173_000 | Any | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlGa__MO_753352975874_000 | Any | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlGe__MO_568124972726_000 | Any | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlIn__MO_944184790819_000 | Any | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlK__MO_412941221384_000 | Any | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlLa__MO_926776289321_000 | Any | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlNb__MO_632661817177_000 | Any | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlPb__MO_770521697914_000 | Any | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlPt__MO_863302581933_000 | Any | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlSi__MO_722793085759_000 | Any | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlSn__MO_170495204252_000 | Any | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlSr__MO_461454518609_000 | Any | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlY__MO_299813494955_000 | Any | Polynomial machine learning potential for Al-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlZn__MO_394039619756_000 | Any | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_AlZr__MO_733064316353_000 | Any | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_BeAl__MO_166223079721_000 | Any | Polynomial machine learning potential for Be-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_BeGe__MO_210562250655_000 | Any | Polynomial machine learning potential for Be-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_BeSi__MO_671634227239_000 | Any | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_BeSn__MO_528555971601_000 | Any | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaAg__MO_840510785458_000 | Any | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaAu__MO_051446997049_000 | Any | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaCu__MO_494823132535_000 | Any | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaGa__MO_620107049977_000 | Any | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaGe__MO_309096382726_000 | Any | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaIn__MO_490765539946_000 | Any | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaPb__MO_672167774288_000 | Any | Polynomial machine learning potential for Ca-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaSc__MO_604912657616_000 | Any | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaSn__MO_940919591360_000 | Any | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaTi__MO_099193110426_000 | Any | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CaZn__MO_058393404652_000 | Any | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CuAg__MO_810898131462_000 | Any | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CuAu__MO_641442516463_000 | Any | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_CuZn__MO_364021352323_000 | Any | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GaAg__MO_847495365448_000 | Any | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GaSn__MO_724605405774_000 | Any | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GeIn__MO_615305547031_000 | Any | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GePb__MO_263929759533_000 | Any | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_GeSn__MO_633956130614_000 | Any | Polynomial machine learning potential for Ge-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KAg__MO_912739399567_000 | Any | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KAu__MO_763201557333_000 | Any | Polynomial machine learning potential for K-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KCu__MO_788133924271_000 | Any | Polynomial machine learning potential for K-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KGe__MO_621167263450_000 | Any | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KIn__MO_201956052069_000 | Any | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KPb__MO_321952889826_000 | Any | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KSn__MO_763046237332_000 | Any | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_KZn__MO_560772228117_000 | Any | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiAg__MO_678095293167_000 | Any | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiAl__MO_673249139557_000 | Any | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiAu__MO_212832054512_000 | Any | Polynomial machine learning potential for Li-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiBa__MO_747079893963_000 | Any | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiBi__MO_891618600784_000 | Any | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiCa__MO_003324915667_000 | Any | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiCu__MO_359470205167_000 | Any | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiGa__MO_015586567903_000 | Any | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiGe__MO_727780094789_000 | Any | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiIn__MO_254197704008_000 | Any | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiMg__MO_967814962810_000 | Any | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiPb__MO_712238882822_000 | Any | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiSi__MO_249506249457_000 | Any | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiSr__MO_624269504264_000 | Any | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiTi__MO_250427208520_000 | Any | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_LiZn__MO_731428135000_000 | Any | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgAg__MO_846084561202_000 | Any | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgAl__MO_692135414275_000 | Any | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgAu__MO_609770801376_000 | Any | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgCa__MO_375704627266_000 | Any | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgCu__MO_972374266607_000 | Any | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgGa__MO_559397211025_000 | Any | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgGe__MO_975215938643_000 | Any | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgIn__MO_788672201407_000 | Any | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgK__MO_772460227899_000 | Any | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgSc__MO_491249768216_000 | Any | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgSi__MO_191149570852_000 | Any | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgSn__MO_044676933142_000 | Any | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgTi__MO_572593306702_000 | Any | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_MgY__MO_261056054736_000 | Any | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaAl__MO_304352549753_000 | Any | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaGa__MO_029516867574_000 | Any | Polynomial machine learning potential for Na-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaGe__MO_227144102853_000 | Any | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaIn__MO_101258978335_000 | Any | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaMg__MO_849680225944_000 | Any | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaPb__MO_997133712697_000 | Any | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaSi__MO_443795178180_000 | Any | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaSn__MO_330957285625_000 | Any | Polynomial machine learning potential for Na-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_NaSr__MO_890525547758_000 | Any | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_PdPt__MO_840290619865_000 | Any | Polynomial machine learning potential for Pd-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiAg__MO_793216813105_000 | Any | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiAu__MO_356624561519_000 | Any | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiCu__MO_360657020498_000 | Any | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiGa__MO_055569328296_000 | Any | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiGe__MO_208231994609_000 | Any | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiIn__MO_610594020340_000 | Any | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiPb__MO_293461895034_000 | Any | Polynomial machine learning potential for Si-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SiZn__MO_220027446851_000 | Any | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SnPb__MO_565983711337_000 | Any | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_SrPb__MO_583185896526_000 | Any | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_TiAl__MO_804233947597_000 | Any | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnAg__MO_919349380638_000 | Any | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnAu__MO_032912808265_000 | Any | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnGa__MO_775572504262_000 | Any | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnGe__MO_081372807655_000 | Any | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnIn__MO_271291203301_000 | Any | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p4_ZnSn__MO_373485141821_000 | Any | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgAu__MO_307904218441_000 | Any | Polynomial machine learning potential for Ag-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgBa__MO_274189772904_000 | Any | Polynomial machine learning potential for Ag-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgIn__MO_955686244929_000 | Any | Polynomial machine learning potential for Ag-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgPb__MO_210258361524_000 | Any | Polynomial machine learning potential for Ag-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AgSn__MO_647213859199_000 | Any | Polynomial machine learning potential for Ag-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlAg__MO_544528803287_000 | Any | Polynomial machine learning potential for Al-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlAu__MO_560298104292_000 | Any | Polynomial machine learning potential for Al-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlBa__MO_239385410084_000 | Any | Polynomial machine learning potential for Al-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlCa__MO_785942179170_000 | Any | Polynomial machine learning potential for Al-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlCu__MO_492256155760_000 | Any | Polynomial machine learning potential for Al-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlGa__MO_064268417216_000 | Any | Polynomial machine learning potential for Al-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlGe__MO_195615071888_000 | Any | Polynomial machine learning potential for Al-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlIn__MO_892901609085_000 | Any | Polynomial machine learning potential for Al-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlK__MO_117036128203_000 | Any | Polynomial machine learning potential for Al-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlLa__MO_054836116096_000 | Any | Polynomial machine learning potential for Al-La developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlNb__MO_908619964765_000 | Any | Polynomial machine learning potential for Al-Nb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlPb__MO_514693435223_000 | Any | Polynomial machine learning potential for Al-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlPt__MO_691428312553_000 | Any | Polynomial machine learning potential for Al-Pt developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlSi__MO_357803532528_000 | Any | Polynomial machine learning potential for Al-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlSn__MO_117347119998_000 | Any | Polynomial machine learning potential for Al-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlSr__MO_827887147173_000 | Any | Polynomial machine learning potential for Al-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlZn__MO_049622537243_000 | Any | Polynomial machine learning potential for Al-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_AlZr__MO_896029608934_000 | Any | Polynomial machine learning potential for Al-Zr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_BeSi__MO_263484070173_000 | Any | Polynomial machine learning potential for Be-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_BeSn__MO_878280291994_000 | Any | Polynomial machine learning potential for Be-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaAg__MO_380064591060_000 | Any | Polynomial machine learning potential for Ca-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaAu__MO_832102410842_000 | Any | Polynomial machine learning potential for Ca-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaCu__MO_551804561646_000 | Any | Polynomial machine learning potential for Ca-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaGa__MO_713738845752_000 | Any | Polynomial machine learning potential for Ca-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaGe__MO_269118185064_000 | Any | Polynomial machine learning potential for Ca-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaIn__MO_271930275971_000 | Any | Polynomial machine learning potential for Ca-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaSc__MO_740127206303_000 | Any | Polynomial machine learning potential for Ca-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaSn__MO_785314086751_000 | Any | Polynomial machine learning potential for Ca-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaTi__MO_187618056597_000 | Any | Polynomial machine learning potential for Ca-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CaZn__MO_980126476271_000 | Any | Polynomial machine learning potential for Ca-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CuAg__MO_782279666022_000 | Any | Polynomial machine learning potential for Cu-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CuAu__MO_095129455988_000 | Any | Polynomial machine learning potential for Cu-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_CuZn__MO_956678513832_000 | Any | Polynomial machine learning potential for Cu-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_GaAg__MO_561805428054_000 | Any | Polynomial machine learning potential for Ga-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_GaSn__MO_581276203955_000 | Any | Polynomial machine learning potential for Ga-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_GeIn__MO_302678710789_000 | Any | Polynomial machine learning potential for Ge-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_GePb__MO_777051957569_000 | Any | Polynomial machine learning potential for Ge-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KAg__MO_846472502163_000 | Any | Polynomial machine learning potential for K-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KGe__MO_189738871515_000 | Any | Polynomial machine learning potential for K-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KIn__MO_954533725355_000 | Any | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |