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 |
|---|---|---|
| PolyMLP_Seko_2022p5_KIn__MO_954533725355_000 | Any | Polynomial machine learning potential for K-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KPb__MO_139125413108_000 | Any | Polynomial machine learning potential for K-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KSn__MO_918677191691_000 | Any | Polynomial machine learning potential for K-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_KZn__MO_959478076724_000 | Any | Polynomial machine learning potential for K-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiAg__MO_268197378794_000 | Any | Polynomial machine learning potential for Li-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiAl__MO_986302882939_000 | Any | Polynomial machine learning potential for Li-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiBa__MO_257932235674_000 | Any | Polynomial machine learning potential for Li-Ba developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiBi__MO_473751855524_000 | Any | Polynomial machine learning potential for Li-Bi developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiCa__MO_412852556834_000 | Any | Polynomial machine learning potential for Li-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiCu__MO_145767057091_000 | Any | Polynomial machine learning potential for Li-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiGa__MO_497030060102_000 | Any | Polynomial machine learning potential for Li-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiGe__MO_804320610366_000 | Any | Polynomial machine learning potential for Li-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiIn__MO_371877881952_000 | Any | Polynomial machine learning potential for Li-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiMg__MO_605808283721_000 | Any | Polynomial machine learning potential for Li-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiPb__MO_540538979060_000 | Any | Polynomial machine learning potential for Li-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiSi__MO_320159332794_000 | Any | Polynomial machine learning potential for Li-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiSr__MO_983192719346_000 | Any | Polynomial machine learning potential for Li-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiTi__MO_326969927623_000 | Any | Polynomial machine learning potential for Li-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_LiZn__MO_350804046613_000 | Any | Polynomial machine learning potential for Li-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgAg__MO_831475505025_000 | Any | Polynomial machine learning potential for Mg-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgAl__MO_229599966843_000 | Any | Polynomial machine learning potential for Mg-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgAu__MO_321125562280_000 | Any | Polynomial machine learning potential for Mg-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgCa__MO_379949544448_000 | Any | Polynomial machine learning potential for Mg-Ca developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgCu__MO_618366915582_000 | Any | Polynomial machine learning potential for Mg-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgGa__MO_008045565605_000 | Any | Polynomial machine learning potential for Mg-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgGe__MO_304107968538_000 | Any | Polynomial machine learning potential for Mg-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgIn__MO_948653803149_000 | Any | Polynomial machine learning potential for Mg-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgK__MO_902620543960_000 | Any | Polynomial machine learning potential for Mg-K developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgSc__MO_718368079956_000 | Any | Polynomial machine learning potential for Mg-Sc developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgSi__MO_994949789313_000 | Any | Polynomial machine learning potential for Mg-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgSn__MO_918607134170_000 | Any | Polynomial machine learning potential for Mg-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgTi__MO_848584635322_000 | Any | Polynomial machine learning potential for Mg-Ti developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_MgY__MO_682292222653_000 | Any | Polynomial machine learning potential for Mg-Y developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaAl__MO_662966024141_000 | Any | Polynomial machine learning potential for Na-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaGe__MO_381273201626_000 | Any | Polynomial machine learning potential for Na-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaIn__MO_398378879035_000 | Any | Polynomial machine learning potential for Na-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaMg__MO_916731735714_000 | Any | Polynomial machine learning potential for Na-Mg developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaPb__MO_873954052557_000 | Any | Polynomial machine learning potential for Na-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaSi__MO_888996916199_000 | Any | Polynomial machine learning potential for Na-Si developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_NaSr__MO_550486674595_000 | Any | Polynomial machine learning potential for Na-Sr developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiAg__MO_112580291189_000 | Any | Polynomial machine learning potential for Si-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiAu__MO_770592401017_000 | Any | Polynomial machine learning potential for Si-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiCu__MO_367629511331_000 | Any | Polynomial machine learning potential for Si-Cu developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiGa__MO_673957672220_000 | Any | Polynomial machine learning potential for Si-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiGe__MO_346818603547_000 | Any | Polynomial machine learning potential for Si-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiIn__MO_486299538452_000 | Any | Polynomial machine learning potential for Si-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SiZn__MO_932217968946_000 | Any | Polynomial machine learning potential for Si-Zn developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SnPb__MO_377488288012_000 | Any | Polynomial machine learning potential for Sn-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_SrPb__MO_163017302633_000 | Any | Polynomial machine learning potential for Sr-Pb developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_TiAl__MO_155544599291_000 | Any | Polynomial machine learning potential for Ti-Al developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnAg__MO_834156792294_000 | Any | Polynomial machine learning potential for Zn-Ag developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnAu__MO_491151589226_000 | Any | Polynomial machine learning potential for Zn-Au developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnGa__MO_909148083054_000 | Any | Polynomial machine learning potential for Zn-Ga developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnGe__MO_571403459169_000 | Any | Polynomial machine learning potential for Zn-Ge developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnIn__MO_657334591346_000 | Any | Polynomial machine learning potential for Zn-In developed by Seko (2022) v000 |
| PolyMLP_Seko_2022p5_ZnSn__MO_851574781118_000 | Any | Polynomial machine learning potential for Zn-Sn developed by Seko (2022) v000 |
| PolyMLP_Seko_2024p1_Ag__MO_278758176027_000 | Any | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Al__MO_599924558257_000 | Any | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_As__MO_078011186186_000 | Any | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Au__MO_664098208305_000 | Any | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ba__MO_132467418614_000 | Any | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Be__MO_394841040550_000 | Any | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Bi__MO_710413125287_000 | Any | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ca__MO_420808095047_000 | Any | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Cd__MO_706111024155_000 | Any | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Cr__MO_691920933145_000 | Any | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Cs__MO_714621609752_000 | Any | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Cu__MO_086541143659_000 | Any | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_CuAgAu__MO_148623624249_000 | Any | Polynomial machine learning potential for Cu-Ag-Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ga__MO_443878836603_000 | Any | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ge__MO_431581350373_000 | Any | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Hf__MO_334350104497_000 | Any | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Hg__MO_202451644950_000 | Any | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_In__MO_245201429139_000 | Any | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ir__MO_664668386247_000 | Any | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_K__MO_199232334901_000 | Any | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_La__MO_115406610268_000 | Any | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Li__MO_405753048966_000 | Any | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Mg__MO_483087121724_000 | Any | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Mo__MO_817818730673_000 | Any | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Na__MO_325879443078_000 | Any | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Nb__MO_502102785787_000 | Any | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Os__MO_811573570744_000 | Any | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_P__MO_194597553958_000 | Any | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Pb__MO_326037826897_000 | Any | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Pd__MO_420470599071_000 | Any | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Pt__MO_577251643083_000 | Any | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Rb__MO_390732290131_000 | Any | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Re__MO_553427721950_000 | Any | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Rh__MO_181269649110_000 | Any | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ru__MO_038250921060_000 | Any | Polynomial machine learning potential for Ru developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Sc__MO_059205513959_000 | Any | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Si__MO_761568271122_000 | Any | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Sn__MO_129294300331_000 | Any | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Sr__MO_706097448505_000 | Any | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ta__MO_153972066617_000 | Any | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Te__MO_841124144524_000 | Any | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Ti__MO_266466295447_000 | Any | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Tl__MO_164437313211_000 | Any | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_V__MO_289723923467_000 | Any | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_W__MO_382353320753_000 | Any | Polynomial machine learning potential for W developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Y__MO_787548276025_000 | Any | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Zn__MO_013942547892_000 | Any | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1_Zr__MO_901319296850_000 | Any | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ag__MO_636501541840_000 | Any | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Al__MO_505948145953_000 | Any | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_As__MO_497481366479_000 | Any | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Au__MO_325811648164_000 | Any | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ba__MO_719111339457_000 | Any | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Be__MO_572696229114_000 | Any | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Bi__MO_155303177087_000 | Any | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Cr__MO_414457089579_000 | Any | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ge__MO_153960636203_000 | Any | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Hf__MO_302706142381_000 | Any | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ir__MO_955623544734_000 | Any | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_La__MO_670429137891_000 | Any | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Mo__MO_171399087989_000 | Any | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Nb__MO_039600711472_000 | Any | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Os__MO_303414393448_000 | Any | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_P__MO_096032629883_000 | Any | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Si__MO_525356107144_000 | Any | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Te__MO_530548424690_000 | Any | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p1hybrid_Ti__MO_856731010667_000 | Any | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ag__MO_935309189068_000 | Any | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Al__MO_741327671619_000 | Any | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_As__MO_277147085224_000 | Any | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Au__MO_308534295960_000 | Any | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ba__MO_754892597872_000 | Any | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Be__MO_938630133784_000 | Any | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Bi__MO_091780912029_000 | Any | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ca__MO_658935632136_000 | Any | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Cd__MO_201385456085_000 | Any | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Cr__MO_405944943357_000 | Any | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Cs__MO_516479295563_000 | Any | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Cu__MO_307870960219_000 | Any | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ga__MO_455323995017_000 | Any | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ge__MO_321247571012_000 | Any | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Hf__MO_767448300017_000 | Any | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Hg__MO_532512458775_000 | Any | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_In__MO_421845913188_000 | Any | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ir__MO_701278475153_000 | Any | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_K__MO_974844744880_000 | Any | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_La__MO_272407985288_000 | Any | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Li__MO_759936102551_000 | Any | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Mg__MO_111117764714_000 | Any | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Mo__MO_057634235224_000 | Any | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Na__MO_520429437472_000 | Any | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Nb__MO_054569351205_000 | Any | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Os__MO_175583695742_000 | Any | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_P__MO_350674028724_000 | Any | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Pb__MO_928219905404_000 | Any | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Pd__MO_372135877195_000 | Any | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Pt__MO_463206825133_000 | Any | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Rb__MO_996823884506_000 | Any | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Re__MO_677754121566_000 | Any | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Rh__MO_955089466881_000 | Any | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ru__MO_057203970637_000 | Any | Polynomial machine learning potential for Ru developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Sc__MO_402404331000_000 | Any | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Si__MO_584800695875_000 | Any | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Sn__MO_941232596453_000 | Any | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Sr__MO_500016842303_000 | Any | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ta__MO_221594740816_000 | Any | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Te__MO_119053663988_000 | Any | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Ti__MO_498704797577_000 | Any | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Tl__MO_328290624408_000 | Any | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_V__MO_935781222742_000 | Any | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_W__MO_130162608338_000 | Any | Polynomial machine learning potential for W developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Y__MO_372004499101_000 | Any | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Zn__MO_915274476867_000 | Any | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2_Zr__MO_455180390661_000 | Any | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ag__MO_102756064984_000 | Any | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Al__MO_473810602608_000 | Any | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_As__MO_079527892021_000 | Any | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Au__MO_310892349221_000 | Any | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ba__MO_372263184744_000 | Any | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Be__MO_788262624433_000 | Any | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Cr__MO_019650365861_000 | Any | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ge__MO_205552143571_000 | Any | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Hf__MO_339993859628_000 | Any | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ir__MO_757141468606_000 | Any | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_La__MO_925272714883_000 | Any | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Mo__MO_618958452179_000 | Any | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Nb__MO_487415899293_000 | Any | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Os__MO_930178745031_000 | Any | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_P__MO_953154730976_000 | Any | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Si__MO_744893990044_000 | Any | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Te__MO_930850065494_000 | Any | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p2hybrid_Ti__MO_972411558592_000 | Any | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ag__MO_240369238264_000 | Any | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Al__MO_880067883971_000 | Any | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_As__MO_273539458220_000 | Any | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Au__MO_410001007835_000 | Any | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ba__MO_132467328236_000 | Any | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Be__MO_932695185972_000 | Any | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Bi__MO_629061695536_000 | Any | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ca__MO_629338023450_000 | Any | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Cd__MO_117089654786_000 | Any | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Cr__MO_737092069690_000 | Any | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Cs__MO_878584542829_000 | Any | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Cu__MO_142845792483_000 | Any | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ga__MO_154173534935_000 | Any | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ge__MO_469192742249_000 | Any | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Hf__MO_493705577112_000 | Any | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Hg__MO_504229735907_000 | Any | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_In__MO_256545325433_000 | Any | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ir__MO_146752984652_000 | Any | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_K__MO_138792914859_000 | Any | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_La__MO_318904455944_000 | Any | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Li__MO_666615010440_000 | Any | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Mg__MO_581222852664_000 | Any | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Mo__MO_214371386462_000 | Any | Polynomial machine learning potential for Mo developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Na__MO_503859980643_000 | Any | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Nb__MO_511596435272_000 | Any | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Os__MO_174802158248_000 | Any | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_P__MO_516340912478_000 | Any | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Pb__MO_298406547550_000 | Any | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Pd__MO_703884683416_000 | Any | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Pt__MO_562889622009_000 | Any | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Rb__MO_298971280178_000 | Any | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Re__MO_196791352634_000 | Any | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Rh__MO_440425426290_000 | Any | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ru__MO_402775474922_000 | Any | Polynomial machine learning potential for Ru developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Sc__MO_382670449972_000 | Any | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Si__MO_073458825522_000 | Any | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Sn__MO_824732483969_000 | Any | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Sr__MO_506937453247_000 | Any | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ta__MO_181313409373_000 | Any | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Te__MO_300992695616_000 | Any | Polynomial machine learning potential for Te developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Ti__MO_182502121600_000 | Any | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Tl__MO_164910812077_000 | Any | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_V__MO_087132030786_000 | Any | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_W__MO_146001374648_000 | Any | Polynomial machine learning potential for W developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Y__MO_263436104699_000 | Any | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Zn__MO_152270635677_000 | Any | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p3_Zr__MO_158197888035_000 | Any | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ag__MO_341373946400_000 | Any | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Al__MO_559410074487_000 | Any | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_As__MO_961870608192_000 | Any | Polynomial machine learning potential for As developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Au__MO_779643048689_000 | Any | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ba__MO_157353590251_000 | Any | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Be__MO_515641496056_000 | Any | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Bi__MO_356657367492_000 | Any | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ca__MO_837602762416_000 | Any | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Cd__MO_350162857967_000 | Any | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Cr__MO_771039881137_000 | Any | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Cs__MO_839179562554_000 | Any | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Cu__MO_745561504899_000 | Any | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ga__MO_801474484441_000 | Any | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ge__MO_042012467922_000 | Any | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Hf__MO_023969363307_000 | Any | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Hg__MO_502986783854_000 | Any | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_In__MO_068363759658_000 | Any | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ir__MO_783343249128_000 | Any | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_K__MO_721514865519_000 | Any | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_La__MO_453035893746_000 | Any | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Li__MO_964367448849_000 | Any | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Mg__MO_455752709662_000 | Any | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Na__MO_456942459102_000 | Any | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Nb__MO_397309129886_000 | Any | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Os__MO_276661364825_000 | Any | Polynomial machine learning potential for Os developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_P__MO_988417608661_000 | Any | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Pb__MO_660303636568_000 | Any | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Pd__MO_462323298833_000 | Any | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Pt__MO_971298066572_000 | Any | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Rb__MO_961004255907_000 | Any | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Re__MO_704581193415_000 | Any | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Rh__MO_653983873948_000 | Any | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Sc__MO_040886226556_000 | Any | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Si__MO_275365833597_000 | Any | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Sn__MO_053108889601_000 | Any | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Sr__MO_886791347263_000 | Any | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ta__MO_326652710145_000 | Any | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Ti__MO_391511922130_000 | Any | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Tl__MO_558479896972_000 | Any | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_V__MO_512441689292_000 | Any | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_W__MO_391151058263_000 | Any | Polynomial machine learning potential for W developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Y__MO_230716576638_000 | Any | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Zn__MO_130747965596_000 | Any | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p4_Zr__MO_724046521842_000 | Any | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ag__MO_341491917374_000 | Any | Polynomial machine learning potential for Ag developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Al__MO_246404761811_000 | Any | Polynomial machine learning potential for Al developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Au__MO_374303863541_000 | Any | Polynomial machine learning potential for Au developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ba__MO_559111961492_000 | Any | Polynomial machine learning potential for Ba developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Be__MO_067637039349_000 | Any | Polynomial machine learning potential for Be developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Bi__MO_338888193464_000 | Any | Polynomial machine learning potential for Bi developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ca__MO_061286710401_000 | Any | Polynomial machine learning potential for Ca developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Cd__MO_564296947902_000 | Any | Polynomial machine learning potential for Cd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Cr__MO_631295096534_000 | Any | Polynomial machine learning potential for Cr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Cs__MO_202257793980_000 | Any | Polynomial machine learning potential for Cs developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Cu__MO_629261612896_000 | Any | Polynomial machine learning potential for Cu developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ga__MO_180554235366_000 | Any | Polynomial machine learning potential for Ga developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ge__MO_905396426131_000 | Any | Polynomial machine learning potential for Ge developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Hf__MO_091439942494_000 | Any | Polynomial machine learning potential for Hf developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Hg__MO_711742369362_000 | Any | Polynomial machine learning potential for Hg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_In__MO_335295448533_000 | Any | Polynomial machine learning potential for In developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ir__MO_779046847081_000 | Any | Polynomial machine learning potential for Ir developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_K__MO_353040166006_000 | Any | Polynomial machine learning potential for K developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_La__MO_537536152313_000 | Any | Polynomial machine learning potential for La developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Li__MO_658935003268_000 | Any | Polynomial machine learning potential for Li developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Mg__MO_457694844885_000 | Any | Polynomial machine learning potential for Mg developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Na__MO_774317753406_000 | Any | Polynomial machine learning potential for Na developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Nb__MO_476137308171_000 | Any | Polynomial machine learning potential for Nb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_P__MO_275217715948_000 | Any | Polynomial machine learning potential for P developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Pb__MO_656650588444_000 | Any | Polynomial machine learning potential for Pb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Pd__MO_836748466719_000 | Any | Polynomial machine learning potential for Pd developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Pt__MO_820541957177_000 | Any | Polynomial machine learning potential for Pt developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Rb__MO_306208717766_000 | Any | Polynomial machine learning potential for Rb developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Re__MO_352844985070_000 | Any | Polynomial machine learning potential for Re developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Rh__MO_391016194938_000 | Any | Polynomial machine learning potential for Rh developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Sc__MO_147759802608_000 | Any | Polynomial machine learning potential for Sc developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Si__MO_334116375414_000 | Any | Polynomial machine learning potential for Si developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Sn__MO_973310469366_000 | Any | Polynomial machine learning potential for Sn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Sr__MO_185342388623_000 | Any | Polynomial machine learning potential for Sr developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ta__MO_544931424312_000 | Any | Polynomial machine learning potential for Ta developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Ti__MO_342885913756_000 | Any | Polynomial machine learning potential for Ti developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Tl__MO_594090581812_000 | Any | Polynomial machine learning potential for Tl developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_V__MO_291696688458_000 | Any | Polynomial machine learning potential for V developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Y__MO_026857916322_000 | Any | Polynomial machine learning potential for Y developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Zn__MO_591828103224_000 | Any | Polynomial machine learning potential for Zn developed by Seko (2024) v000 |
| PolyMLP_Seko_2024p5_Zr__MO_304394077411_000 | Any | Polynomial machine learning potential for Zr developed by Seko (2024) v000 |
| QUIP_GAP_SivaramanGallingtonKrishnamoorthy_2021_HfO__MO_200178964232_000 | Any | GAP model for Hf and O developed by Sivaraman, Gallington, and Krishnamoorthy et al. (2021) v000 |
| QUIP_GAP_SivaramanGuoWard_2021_LiCl__MO_225395104084_000 | Any | QUIP GAP potential developed by Sivaraman et al. for modeling molten LiCl salt (2021) v000 |
| QUIP_GAP_Xu_2003_Pt__MO_370837021112_000 | Any | GAP model for Pt developed by Xu (2023) v000 |
| Sim_LAMMPS_ADP_ApostolMishin_2011_AlCu__SM_667696763561_000 | LAMMPS | LAMMPS ADP potential for Al-Cu developed by Apostol and Mishin (2011) v000 |
| Sim_LAMMPS_ADP_HowellsMishin_2018_Cr__SM_884076133432_000 | LAMMPS | LAMMPS ADP potential for Cr developed by Howells and Mishin (2018) v000 |
| Sim_LAMMPS_ADP_MishinMehlPapaconstantopoulos_2005_Ni__SM_477692857359_000 | LAMMPS | LAMMPS ADP Potential for Ni developed by Mishin et al. (2005) v000 |
| Sim_LAMMPS_ADP_PunDarlingKecskes_2015_CuTa__SM_399364650444_000 | LAMMPS | LAMMPS ADP potential for the Cu-Ta system developed by Pun et al. (2015) v000 |
| Sim_LAMMPS_ADP_SmirnovaStarikov_2017_ZrNb__SM_937902197407_000 | LAMMPS | LAMMPS ADP potential for the Zr-Nb system developed by Smirnova and Starikov (2017) v000 |
| Sim_LAMMPS_ADP_SmirnovaStarikovVlasova_2018_MgH__SM_899925688973_000 | LAMMPS | LAMMPS ADP potential for the Mg-H system developed by Smirnova, Starikov and Vlasova (2018) v000 |
| Sim_LAMMPS_ADP_StarikovGordeevLysogorskiy_2020_SiAuAl__SM_113843830602_000 | LAMMPS | LAMMPS ADP potential for the Si-Au-Al system developed by Starikov et al. (2020) v000 |
| Sim_LAMMPS_ADP_StarikovKolotovaKuksin_2017_UMo__SM_682749584055_000 | LAMMPS | LAMMPS ADP potential for the U-Mo system developed by Starikov et al. (2017) v000 |
| Sim_LAMMPS_ADP_StarikovLopanitsynaSmirnova_2018_SiAu__SM_985135773293_000 | LAMMPS | LAMMPS ADP potential for the Si-Au system developed by Starikov et al. (2018) v000 |
| Sim_LAMMPS_ADP_StarikovSmirnova_2021_ZrNb__SM_993852507257_000 | LAMMPS | LAMMPS ADP potential for the Zr-Nb system developed by Starikov and Smirnova (2021) v000 |
| Sim_LAMMPS_ADP_StarikovSmirnovaPradhan_2021_Fe__SM_906654900816_000 | LAMMPS | LAMMPS ADP potential for Fe developed by Starikov et al. (2021) v000 |
| Sim_LAMMPS_ADP_TseplyaevStarikov_2016_UN__SM_474015477315_000 | LAMMPS | LAMMPS ADP potential for the U-N system developed by Tseplyaev and Starikov (2016) v000 |
| Sim_LAMMPS_ADP_WangXuQian_2021_AuRh__SM_066295357485_000 | LAMMPS | LAMMPS ADP potential for the Au-Rh system developed by Wang et al. (2021) v000 |
| Sim_LAMMPS_ADP_XuWangQian_2022_NiPd__SM_559286646876_000 | LAMMPS | LAMMPS ADP potential for the Ni-Pd system developed by Xu et al. (2022) v000 |
| Sim_LAMMPS_ADP_XuWangQian_2022_NiRh__SM_306597220004_000 | LAMMPS | LAMMPS ADP potential for the Ni-Rh system developed by Xu et al. (2022) v000 |
| Sim_LAMMPS_AGNI_BotuBatraChapman_2017_Al__SM_666183636896_000 | LAMMPS | LAMMPS AGNI potential for Al developed by Botu et al. (2017) v000 |
| Sim_LAMMPS_AGNI_BotuRamprasad_2015_Al__SM_526060833691_000 | LAMMPS | LAMMPS AGNI potential for Al developed by Botu and Ramprasad (2015) v000 |
| Sim_LAMMPS_AIREBO_BondCentric_HurStuart_2012_CH__SM_727404582899_000 | LAMMPS | LAMMPS AIREBO-BC (bond-centric AIREBO) potential for C-H developed by Hur and Stuart (2012) v000 |
| Sim_LAMMPS_AIREBO_LJ_StuartTuteinHarrison_2000_CH__SM_069621990420_000 | LAMMPS | LAMMPS AIREBO-LJ potential for C-H developed by Stuart, Tutein, and Harrison (2000) v000 |
| Sim_LAMMPS_AIREBO_Morse_OConnorAndzelmRobbins_2015_CH__SM_460187474631_000 | LAMMPS | LAMMPS AIREBO-M potential for C-H developed by O'Connor, Andzelm, and Robbins (2015) v000 |
| Sim_LAMMPS_BOP_MurdickZhouWadley_2006_GaAs__SM_104202807866_001 | LAMMPS | LAMMPS BOP potential for the Ga-As system developed by Murdick et al. (2006) v001 |
| Sim_LAMMPS_BOP_WardZhouWong_2012_CdTe__SM_509819366101_001 | LAMMPS | LAMMPS BOP potential for the Cd-Te system developed by Ward et al. (2012) v001 |
| Sim_LAMMPS_BOP_WardZhouWong_2012_CdZnTe__SM_409035133405_001 | LAMMPS | LAMMPS BOP potential for the Cd-Zn-Te system developed by Ward et al. (2012) v001 |
| Sim_LAMMPS_BOP_WardZhouWong_2013_CdZnTe__SM_010061267051_000 | LAMMPS | LAMMPS BOP potential for the Cd-Zn-Te system developed by Ward et al. (2013) v000 |
| Sim_LAMMPS_BOP_ZhouFosterVanSwol_2014_CdTeSe__SM_567065323363_000 | LAMMPS | LAMMPS BOP potential for the Cd-Te-Se system developed by Zhou et al. (2014) v000 |
| Sim_LAMMPS_BOP_ZhouWardFoster_2015_CCu__SM_784926969362_000 | LAMMPS | LAMMPS BOP potential for the C-Cu system developed by Zhou, Ward, and Foster (2015) v000 |
| Sim_LAMMPS_BOP_ZhouWardFoster_2015_CuH__SM_404135993060_000 | LAMMPS | LAMMPS BOP potential for the Cu-H system developed by Zhou et al. (2015) v000 |
| Sim_LAMMPS_BOP_ZhouWardFoster_2016_AlCu__SM_566399258279_001 | LAMMPS | LAMMPS BOP potential for the Al-Cu system developed by Zhou, Ward, and Foster (2016) v001 |
| Sim_LAMMPS_BOP_ZhouWardFoster_2018_AlCuH__SM_834012669168_000 | LAMMPS | LAMMPS BOP potential for the Al-Cu-H system developed by Zhou, Ward and Foster (2018) v000 |
| Sim_LAMMPS_Buckingham_ArimaYamasakiTorikai_2005_CeO__SM_328512278696_000 | LAMMPS | LAMMPS Buckingham potential for CeO2 developed by Arima et al (2005) v000 |
| Sim_LAMMPS_Buckingham_ArimaYoshidaMatsumoto_2014_PuUThNpO__SM_182981756100_000 | LAMMPS | LAMMPS Buckingham potential for MOX oxides developed by Arima et al (2014) v000 |
| Sim_LAMMPS_Buckingham_CarreHorbachIspas_2008_SiO__SM_886641404623_000 | LAMMPS | LAMMPS Buckingham potential for SiO2 developed by Carré et al. (2008) v000 |
| Sim_LAMMPS_Buckingham_FangKeltyHe_2014_LaO__SM_576027677976_000 | LAMMPS | LAMMPS Buckingham potential for La2O3 developed by Fang et al (20014) v000 |
| Sim_LAMMPS_Buckingham_FisherMatsubara_2005_NiO__SM_337243826931_000 | LAMMPS | LAMMPS Buckingham potential for NiO developed by Fisher and Matsubara (2005) v000 |
| Sim_LAMMPS_Buckingham_FreitasSantosColaco_2015_SiCaOAl__SM_154093256665_000 | LAMMPS | LAMMPS Buckingham potential for CaO–Al2O3–SiO2 systems developed by Freitas et al. (2015) v000 |
| Sim_LAMMPS_Buckingham_GhoshSomayajuluArya_2015_ThCeO__SM_681317476351_000 | LAMMPS | LAMMPS Buckingham potential for (Th,Ce)O2 mized oxides developed by Ghosh et al (2005) v000 |
| Sim_LAMMPS_Buckingham_GuillotSator_2007_OSiTiAlFeMgCaNaK__SM_025794816176_000 | LAMMPS | LAMMPS Buckingham potential for K2O–Na2O–CaO–MgO–Fe2O3–Al2O3–TiO2–SiO2 systems developed by Guillot and Sator (2006) v000 |
| Sim_LAMMPS_Buckingham_Matsui_1994_MgCaAlSiO__SM_082977958669_000 | LAMMPS | LAMMPS Buckingham potential for CaO‐MgO‐Al2O3‐SiO2 systems developed by Matsui (1994) v000 |
| Sim_LAMMPS_Buckingham_MatsuiAkaogi_1991_TiO__SM_690504433912_000 | LAMMPS | LAMMPS Buckingham potential for TiO2 developed by Matsui and Akaogi (1991) v000 |
| Sim_LAMMPS_Buckingham_MomenzadehBelovaMurch_2021_ZrYO__SM_376275128969_000 | LAMMPS | LAMMPS Buckingham potential for yttria-stabilized zirconia by Momenzadeh et al (2021) v000 |
| Sim_LAMMPS_Buckingham_PotashnikovBoyarchenkovNekrasov_2011_PuUO__SM_422015835006_000 | LAMMPS | LAMMPS Buckingham potential for (U,Pu)O2 materials developed by Potashnikov et al (2011) v000 |
| Sim_LAMMPS_Buckingham_SayleCatlowMaphanga_2005_MnO__SM_757974494010_000 | LAMMPS | LAMMPS Buckingham potential for MnO2 developed by Sayle et al. (2005) v000 |
| Sim_LAMMPS_Buckingham_SunStirnerHagston_2006_AlO__SM_466046725502_000 | LAMMPS | LAMMPS Buckingham potential for a-Al2O3 developed by Sun et al. (2006) v000 |
| Sim_LAMMPS_Buckingham_SunStirnerHagston_2006_MgO__SM_152356670345_000 | LAMMPS | LAMMPS Buckingham potential for MgO developed by Sun et al. (2006) v000 |
| Sim_LAMMPS_Buckingham_Vaari_2015_FeO__SM_672759489721_000 | LAMMPS | LAMMPS Buckingham potential for a-Fe2O3 (hematite) reported by Vaari (2015) v000 |
| Sim_LAMMPS_Buckingham_WangShinShin_2019_CrO__SM_295921111679_000 | LAMMPS | LAMMPS Buckingham potential for Cr2O3 reported by Wang, Shin and Shin (2019) v000 |
| Sim_LAMMPS_CoreShell_MitchellFincham_1993_CaF__SM_676649151762_000 | LAMMPS | LAMMPS adiabatic core-shell model for the Ca-F system developed by Mitchell and Fincham (1993) v000 |
| Sim_LAMMPS_CoreShell_MitchellFincham_1993_MgO__SM_579243392924_000 | LAMMPS | LAMMPS adiabatic core-shell model for the Mg-O system developed by Mitchell and Fincham (1993) v000 |
| Sim_LAMMPS_CoreShell_MitchellFincham_1993_NaCl__SM_672022050407_000 | LAMMPS | LAMMPS adiabatic core-shell model for the Na-Cl system developed by Mitchell and Fincham (1993) v000 |
| Sim_LAMMPS_EAM_BonnyBakaevTerentyev_2022_FeCCr__SM_747012431642_000 | LAMMPS | LAMMPS EAM Potential developed by Bonny, Bakaev, and Terentyev to study radiation-induced defects in Cr steel (2022) v000 |
| Sim_LAMMPS_EAM_BonnyCastinBullens_2013_FeCrW__SM_699257350704_001 | LAMMPS | LAMMPS EAM potential for Fe-Cr-W developed by Bonny et al. (2013) v001 |
| Sim_LAMMPS_EAM_BonnyPasianotTerentyev_2011_FeCr__SM_237089298463_001 | LAMMPS | LAMMPS EAM potential for Fe-Cr developed by Bonny et al. (2011) v001 |
| Sim_LAMMPS_EAM_EichBeinkeSchmitz_2015_FeCr__SM_731771351835_000 | LAMMPS | EAM/TBM potential for Fe–Cr developed by Eich et al. (2015) v000 |
| Sim_LAMMPS_EAMCD_StukowskiSadighErhart_2009_FeCr__SM_775564499513_000 | LAMMPS | LAMMPS Concentration-Dependent EAM potential for Fe-Cr developed by Stukowski et al. (2009) v000 |
| Sim_LAMMPS_EDIP_LucasBertolusPizzagalli_2009_SiC__SM_435704953434_000 | LAMMPS | LAMMPS EDIP potential for Si-C developed by Lucas, Bertolus, and Pizzagalli (2009) v000 |
| Sim_LAMMPS_EIM_Zhou_2010_BrClCsFIKLiNaRb__SM_259779394709_001 | LAMMPS | LAMMPS EIM potential for the Br-Cl-Cs-F-I-K-Li-Na-Rb system developed by Zhou (2010) v001 |
| Sim_LAMMPS_ExTeP_LosKroesAlbe_2017_BN__SM_692329995993_001 | LAMMPS | ExTeP potential for B-N developed by Los et al. (2017) v001 |
| Sim_LAMMPS_GW_GaoWeber_2002_SiC__SM_606253546840_000 | LAMMPS | LAMMPS Gao-Weber potential for Si-C developed by Gao and Weber (2002) v000 |
| Sim_LAMMPS_GWZBL_Samolyuk_2016_SiC__SM_720598599889_000 | LAMMPS | LAMMPS Gao-Weber potential combined with a modified repulsive ZBL core function for the Si-C system developed by German Samolyuk (2016) v000 |
| Sim_LAMMPS_Hybrid_DuanXieGuo_2019_TaHe__SM_016305073020_001 | LAMMPS | LAMMPS hybrid table and EAM potential for the Ta-He system developed by Duan et al. (2019) v001 |
| Sim_LAMMPS_HybridOverlay_BelandLuOsetskiy_2016_CoNi__SM_445377835613_001 | LAMMPS | LAMMPS hybrid overlay EAM and ZBL potential for the Ni-Co system developed by Beland et al. (2016) v001 |
| Sim_LAMMPS_IFF_CHARMM_GUI_HeinzLinMishra_2023_Nanomaterials__SM_232384752957_000 | LAMMPS | Interface Force Field (IFF) parameters due to Heinz et al. as used in the CHARMM-GUI input generator v000 |
| Sim_LAMMPS_IFF_PCFF_HeinzMishraLinEmami_2015Ver1v5_FccmetalsMineralsSolventsPolymers__SM_039297821658_001 | LAMMPS | LAMMPS PCFF bonded force-field combined with IFF non-bonded 9-6 Lennard-Jones potentials for metal interactions v001 |
| Sim_LAMMPS_LCBOP_LosFasolino_2003_C__SM_469631949122_000 | LAMMPS | LAMMPS LCBOP potential for C developed by Los and Fasolino (2003) v000 |
| Sim_LAMMPS_MEAM_AlmyrasSangiovanniSarakinos_2019_NAlTi__SM_871795249052_000 | LAMMPS | LAMMPS MEAM potential for the Ti-Al-N system developed by Almyras et al. v000 |
| Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Cu__SM_239791545509_000 | LAMMPS | LAMMPS MEAM potential for Cu developed by Asadi et al. (2015) v000 |
| Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Fe__SM_042630680993_001 | LAMMPS | LAMMPS MEAM potential for Fe developed by Asadi et al. (2015) v001 |
| Sim_LAMMPS_MEAM_AsadiZaeemNouranian_2015_Ni__SM_078420412697_001 | LAMMPS | LAMMPS MEAM potential for Ni developed by Asadi et al. (2015) v001 |
| Sim_LAMMPS_MEAM_CuiGaoCui_2012_LiSi__SM_562938628131_000 | LAMMPS | LAMMPS MEAM potential for Li-Si alloys developed by Cui et al. (2012) v000 |
| Sim_LAMMPS_MEAM_DuLenoskyHennig_2011_Si__SM_662785656123_000 | LAMMPS | LAMMPS Spline-based MEAM potential for Si system developed by Du et al. (2011) v000 |
| Sim_LAMMPS_MEAM_EtesamiAsadi_2018_Cu__SM_316120381362_001 | LAMMPS | LAMMPS MEAM potential for Cu developed by Etesami and Asadi (2018) v001 |
| Sim_LAMMPS_MEAM_EtesamiAsadi_2018_Fe__SM_267016608755_001 | LAMMPS | LAMMPS MEAM potential for Fe developed by Etesami and Asadi (2018) v001 |
| Sim_LAMMPS_MEAM_EtesamiAsadi_2018_Ni__SM_333792531460_001 | LAMMPS | LAMMPS MEAM potential for Ni developed by Etesami and Asadi (2018) v001 |
| Sim_LAMMPS_MEAM_FernandezPascuet_2014_U__SM_176800861722_000 | LAMMPS | LAMMPS MEAM potential for U developed by Fernández and Pascuet (2014) v000 |
| Sim_LAMMPS_MEAM_GaoOterodelaRozaAouadi_2013_AgTaO__SM_485325656366_001 | LAMMPS | LAMMPS MEAM potential for perovskite silver tantalate (AgTaO3) developed by Gao et al. (2013) v001 |
| Sim_LAMMPS_MEAM_HennigLenoskyTrinkle_2008_Ti__SM_318953488749_000 | LAMMPS | LAMMPS MEAM potential for Ti developed by Hennig et al. (2008) v000 |
| Sim_LAMMPS_MEAM_JelinekGrohHorstemeyer_2012_AlSiMgCuFe__SM_656517352485_000 | LAMMPS | LAMMPS MEAM potential for Al-Si-Mg-Cu-Fe alloys developed by Jelinek et al. (2012) v000 |
| Sim_LAMMPS_MEAM_KimJungLee_2009_FeTiC__SM_531038274471_000 | LAMMPS | LAMMPS MEAM potential for Fe-Ti-C developed by Kim, Jung, and Lee (2009) v000 |
| Sim_LAMMPS_MEAM_KoGrabowskiNeugebauer_2015_NiTi__SM_770142935022_000 | LAMMPS | LAMMPS MEAM potential for Ni-Ti developed by Ko, Grabowski, and Neugebauer (2015) v000 |
| Sim_LAMMPS_MEAM_Lenosky_2017_W__SM_631352869360_000 | LAMMPS | LAMMPS MEAM Potential for W developed by Lenosky (2017) v000 |
| Sim_LAMMPS_MEAM_LenoskySadighAlonso_2000_Si__SM_622320990752_000 | LAMMPS | LAMMPS MEAM potential for Si system developed by Lenosky et al. (2000) v000 |
| Sim_LAMMPS_MEAM_LiyanageKimHouze_2014_FeC__SM_652425777808_001 | LAMMPS | LAMMPS MEAM potential for Fe-C developed by Liyanage et al. (2014) v001 |
| Sim_LAMMPS_MEAM_MaiselKoZhang_2017_VNiTi__SM_971529344487_000 | LAMMPS | LAMMPS MEAM potential for V-Ni-Ti developed by Maisel et al. (2017) v000 |
| Sim_LAMMPS_MEAM_ParkFellingerLenosky_2012_Mo__SM_769176993156_000 | LAMMPS | LAMMPS MEAM Potential for Mo developed by Park et al. (2012) v000 |
| Sim_LAMMPS_MEAM_ParkFellingerLenosky_2012_Ta__SM_907764821792_000 | LAMMPS | LAMMPS MEAM Potential for Ta developed by Park et al. (2012) v000 |
| Sim_LAMMPS_MEAM_ParkFellingerLenosky_2012_W__SM_163270462402_000 | LAMMPS | LAMMPS MEAM Potential for W developed by Park et al. (2012) v000 |
| Sim_LAMMPS_MEAM_PascuetFernandez_2015_Al__SM_811588957187_000 | LAMMPS | LAMMPS MEAM potential for Al developed by Pascuet and Fernandez (2015) v000 |
| Sim_LAMMPS_MEAM_PascuetFernandez_2015_AlU__SM_721930391003_000 | LAMMPS | LAMMPS MEAM potential for Al-U developed by Pascuet and Fernandez (2015) v000 |
| Sim_LAMMPS_MEAM_VellaChenStillinger_2017_Sn__SM_629915663723_000 | LAMMPS | LAMMPS MEAM potential for liquid Sn developed by Vella et al. (2017) v000 |
| Sim_LAMMPS_MEAM_Wagner_2007_Cu__SM_521856783904_000 | LAMMPS | LAMMPS MEAM potential for Cu developed by Wagner (2007) v000 |
| Sim_LAMMPS_MEAM_Wagner_2007_Ni__SM_168413969663_000 | LAMMPS | LAMMPS MEAM potential for Ni developed by Wagner (2007) v000 |
| Sim_LAMMPS_MEAM_Wagner_2007_SiC__SM_264944083668_000 | LAMMPS | LAMMPS MEAM potential for Si-C developed by Wagner (2007) v000 |
| Sim_LAMMPS_MEAM_ZhangTrinkle_2016_TiO__SM_513612626462_000 | LAMMPS | LAMMPS MEAM potential for the Ti-O system developed by Zhang and Trinkle (2016) v000 |
| Sim_LAMMPS_ModifiedTersoff_ByggmastarHodilleFerro_2018_BeO__SM_305223021383_000 | LAMMPS | LAMMPS Modified Tersoff potential for Be-O developed by Byggmästar et al. (2018) v000 |
| Sim_LAMMPS_ModifiedTersoff_KumagaiIzumiHara_2007_Si__SM_773333226968_000 | LAMMPS | LAMMPS Modified Tersoff potential for Si by Kumagai et al. (2007) v000 |
| Sim_LAMMPS_ModifiedTersoff_PurjaPunMishin_2017_Si__SM_184524061456_000 | LAMMPS | LAMMPS Modified Tersoff potential for Si developed by Purja Pun and Mishin (2017) v000 |
| Sim_LAMMPS_Polymorphic_BereSerra_2006_GaN__SM_518345582208_000 | LAMMPS | LAMMPS Stillinger-Weber potential for the Ga-N system developed by Bere and Serra (2006) and implemented using the polymorphic framework of Zhou et al. (2015) v000 |
| Sim_LAMMPS_Polymorphic_NordAlbeErhart_2003_GaN__SM_333071728528_000 | LAMMPS | LAMMPS BOP potential for the Ga-N system developed by Nord et al. (2003) and implemented using the polymorphic framework of Zhou et al. (2015) v000 |
| Sim_LAMMPS_Polymorphic_Zhou_2004_CuTa__SM_453737875254_000 | LAMMPS | LAMMPS EAM potential for the Cu-Ta system developed by Zhou et al. (2004) and implemented using the polymorphic framework of Zhou et al. (2015) v000 |
| Sim_LAMMPS_Polymorphic_ZhouJonesChu_2017_GaInN__SM_887684855692_000 | LAMMPS | LAMMPS Stillinger-Weber potential for the In-Ga-N system developed by Zhou, Jones and Chu (2017) and implemented using the polymorphic framework of Zhou et al. (2015) v000 |
| Sim_LAMMPS_ReaxFF_AnGoddard_2015_BC__SM_389039364091_000 | LAMMPS | LAMMPS ReaxFF potential for B4C developed by An and Goddard (2015) v000 |
| Sim_LAMMPS_ReaxFF_AryanpourVanDuinKubicki_2010_FeHO__SM_222964216001_001 | LAMMPS | LAMMPS ReaxFF potential for Fe-H-O systems developed by Aryanpour, van Duin, and Kubicki (2010) v001 |
| Sim_LAMMPS_ReaxFF_BroqvistKullgrenWolf_2015_CeO__SM_063950220736_000 | LAMMPS | LAMMPS ReaxFF potential for Ce-O systems developed by Broqvist et al. (2015) v000 |
| Sim_LAMMPS_ReaxFF_BrugnoliMiyataniAkaji_SiCeNaClHO_2023__SM_282799919035_000 | LAMMPS | LAMMPS ReaxFF potential for Ceria/Silica/Water/NaCl developed by Brugnoli et al. (2023) v000 |
| Sim_LAMMPS_ReaxFF_ChenowethVanDuinGoddard_2008_CHO__SM_584143153761_001 | LAMMPS | LAMMPS ReaxFF potential for hydrocarbon oxidation (C-H-O) developed by Chenoweth, van Duin, and Goddard (2008) v001 |
| Sim_LAMMPS_ReaxFF_ChenowethVanDuinPersson_2008_CHOV__SM_429148913211_001 | LAMMPS | LAMMPS ReaxFF potential for reactions between hydrocarbons and vanadium oxide clusters (C-H-O-V) developed by Chenoweth et al. (2008) v001 |
| Sim_LAMMPS_reaxFF_FthenakisPetsalakisTozzini_2022_CHON__SM_198543900691_000 | LAMMPS | LAMMPS ReaxFF potential for C-H-N-O systems developed by Fthenakis et al. (2022) v001 |
| Sim_LAMMPS_ReaxFF_IslamOstadhosseinBorodin_2015_LiS__SM_058492438145_000 | LAMMPS | LAMMPS ReaxFF potential for Li-S systems developed by Islam et al. (2014) v000 |
| Sim_LAMMPS_ReaxFF_KeithFantauzziJacob_2010_AuO__SM_974345878378_001 | LAMMPS | LAMMPS ReaxFF potential for Au-O systems developed by Keith et al. (2010) v001 |
| Sim_LAMMPS_ReaxFF_ManzanoMoeiniMarinelli_2012_CaSiOH__SM_714124634215_000 | LAMMPS | LAMMPS ReaxFF potential for Ca-Si-O-H systems developed by Manzano et al. (2012) v000 |
| Sim_LAMMPS_ReaxFF_PolsVincentLunaFilot_2021_CsPbI__SM_367523551183_000 | LAMMPS | LAMMPS ReaxFF potential for CsPbI3 developed by Pols et al (2021) v000 |
| Sim_LAMMPS_ReaxFF_RaymandVanDuinBaudin_2008_ZnOH__SM_449472104549_001 | LAMMPS | ReaxFF potential for Zn-O-H systems developed by Raymand et al. (2008) v001 |
| Sim_LAMMPS_ReaxFF_SinghSrinivasanNeekAmal_2013_CFH__SM_306840588959_000 | LAMMPS | LAMMPS ReaxFF potential for fluorographene (C-F-H) developed by Singh et al. (2013) v000 |
| Sim_LAMMPS_ReaxFF_StrachanVanDuinChakraborty_2003_CHNO__SM_107643900657_001 | LAMMPS | LAMMPS ReaxFF potential for RDX (C-H-N-O) systems developed by Strachan et al. (2003) v001 |
| Sim_LAMMPS_ReaxFF_WeismillerVanDuinLee_2010_BHNO__SM_327381922729_001 | LAMMPS | LAMMPS ReaxFF potential for Ammonia Borane (B-H-N-O) developed by Weismiller et al. (2010) v001 |
| Sim_LAMMPS_ReaxFF_XiaoShiHao_2017_PHOC__SM_424780295507_000 | LAMMPS | LAMMPS ReaxFF transferable potential for P/H/O/C systems with application to phosphorene developed by Xiao et al. (2017) v000 |
| Sim_LAMMPS_SMTBQ_SallesPolitanoAmzallag_2016_Al__SM_404097633924_000 | LAMMPS | LAMMPS SMTBQ potential for Al developed by Salles et al. (2016) v000 |
| Sim_LAMMPS_SMTBQ_SallesPolitanoAmzallag_2016_AlO__SM_853967355976_000 | LAMMPS | LAMMPS SMTBQ potential for the Al-O system developed by Salles et al. (2016) v000 |
| Sim_LAMMPS_SMTBQ_SallesPolitanoAmzallag_2016_TiO__SM_349577644423_000 | LAMMPS | LAMMPS SMTBQ potential for the Ti-O system developed by Salles et al. (2016) v000 |
| Sim_LAMMPS_SNAP_ChenDengTran_2017_Mo__SM_003882782678_000 | LAMMPS | LAMMPS SNAP potential for Mo developed by Chen et al. (2017) v000 |
| Sim_LAMMPS_Table_GrogerVitekDlouhy_2020_CoCrFeMnNi__SM_786004631953_001 | LAMMPS | LAMMPS tabular pair potential for the Co-Cr-Fe-Mn-Ni system developed by Groger, Vitek and Dlouhy (2020) v001 |
| Sim_LAMMPS_TersoffZBL_ByggmastarGranberg_2020_Fe__SM_958863895234_000 | LAMMPS | LAMMPS Tersoff-ZBL potential for Fe developed by J. Byggmästar and Granberg (2020) v000 |
| Sim_LAMMPS_TersoffZBL_DevanathanDiazdelaRubiaWeber_1998_SiC__SM_578912636995_000 | LAMMPS | LAMMPS Tersoff-ZBL potential for Si-C developed by Devanathan, Diaz de la Rubia, and Weber (1998) v000 |
| Sim_LAMMPS_TersoffZBL_HenrikssonBjorkasNordlund_2013_FeC__SM_473463498269_000 | LAMMPS | LAMMPS Tersoff-ZBL potential for Fe-C developed by Henriksson, Björkas and Nordlund (2013) v000 |
| Sim_LAMMPS_Vashishta_BranicioRinoGan_2009_InP__SM_090647175366_000 | LAMMPS | LAMMPS Vashishta potential for the In-P system developed by Branicio et al. (2009) v000 |
| Sim_LAMMPS_Vashishta_BroughtonMeliVashishta_1997_SiO__SM_422553794879_000 | LAMMPS | LAMMPS Vashishta potential for the Si-O system developed by Broughton et al. (1997) v000 |
| Sim_LAMMPS_Vashishta_NakanoKaliaVashishta_1994_SiO__SM_503555646986_000 | LAMMPS | LAMMPS Vashishta potential for the Si-O system developed by Nakano et al. (1994) v000 |
| Sim_LAMMPS_Vashishta_VashishtaKaliaNakano_2007_SiC__SM_196548226654_000 | LAMMPS | LAMMPS Vashishta potential for the Si-C system developed by Vashishta et al. (2007) v000 |
| Sim_LAMMPS_Vashishta_VashishtaKaliaRino_1990_SiO__SM_887826436433_000 | LAMMPS | LAMMPS Vashishta potential for the Si-O system developed by Vashishta et al. (1990) v000 |
| SNAP_ChenDengTran_2017_Mo__MO_698578166685_000 | Any | A spectral neighbor analysis potential for Mo developed by Chi Chen (2019) v000 |
| SNAP_LiChenZheng_2019_NbTaWMo__MO_560387080449_000 | Any | A spectral neighbor analysis potential for Nb-Mo-Ta-W developed by Xiangguo Li (2019) v000 |
| SNAP_LiHuChen_2018_Cu__MO_529419924683_000 | Any | A spectral neighbor analysis potential for Cu developed by Xiangguo Li (2019) v000 |
| SNAP_LiHuChen_2018_Ni__MO_913991514986_000 | Any | A spectral neighbor analysis potential for Ni developed by Xiangguo Li (2019) v000 |
| SNAP_LiHuChen_2018_NiMo__MO_468686727341_000 | Any | A spectral neighbor analysis potential for Ni-Mo developed by Xiangguo Li (2019) v000 |
| SNAP_ThompsonSwilerTrott_2015_Ta__MO_359768485367_000 | Any | Spectral Neighbor Analysis Potential (SNAP) for tantalum developed by Thompson, Swiler, Trott, et al. (2015) v000 |
| SNAP_WoodCusentinoWirth_2019_WBe__MO_939388497041_000 | Any | A spectral neighbor analysis potential for W-Be developed by Wood et al. (2019) v000 |
| SNAP_ZuoChenLi_2019_Cu__MO_931672895580_000 | Any | A spectral neighbor analysis potential for Cu developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Ge__MO_183216355174_000 | Any | A spectral neighbor analysis potential for Ge developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Li__MO_732106099012_000 | Any | A spectral neighbor analysis potential for Li developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Mo__MO_014123846623_000 | Any | A spectral neighbor analysis potential for Mo developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Ni__MO_365106510449_000 | Any | A spectral neighbor analysis potential for Ni developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019_Si__MO_869330304805_000 | Any | A spectral neighbor analysis potential for Si developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Cu__MO_265210066873_000 | Any | A quadratic spectral neighbor analysis potential for Cu developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Ge__MO_766484508139_000 | Any | A quadratic spectral neighbor analysis potential for Ge developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Li__MO_041269750353_000 | Any | A quadratic spectral neighbor analysis potential for Li developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Mo__MO_692442138123_000 | Any | A quadratic spectral neighbor analysis potential for Mo developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Ni__MO_263593395744_000 | Any | A quadratic spectral neighbor analysis potential for Ni developed by Yunxing Zuo v000 |
| SNAP_ZuoChenLi_2019quadratic_Si__MO_721469752060_000 | Any | A quadratic spectral neighbor analysis potential for Si developed by Yunxing Zuo v000 |
| SW_BalamaneHaliciogluTiller_1992_Si__MO_113686039439_005 | Any | Stillinger-Weber potential for Si developed by Balamane, Halicioglu and Tiller (1992) v005 |
| SW_BalamaneHauchShi_2017Brittle_Si__MO_381114941873_003 | Any | Stillinger-Weber potential for brittle Si combining the modifications of Balamane et al. (1992) and Hauch et al. (1999) v003 |
| SW_BereSerra_2006_GaN__MO_861114678890_001 | Any | Stillinger-Weber potential for the Ga-N system developed by Bere and Serra (2006) v001 |
| SW_DingAndersen_1986_Ge__MO_775478537242_000 | Any | Stillinger-Weber potential for crystalline and amorphous Ge as well as germanene due to Ding and Andersen (1986) v000 |
| SW_HauchHollandMarder_1999Brittle_Si__MO_119167353542_005 | Any | Stillinger-Weber potential for brittle Si due to Hauch et al. (1999) v005 |
| SW_LeeHwang_2012GGA_Si__MO_040570764911_001 | Any | Stillinger-Weber potential for Si optimized for thermal conductivity due to Lee and Hwang (1985); GGA parameterization v001 |
| SW_LeeHwang_2012LDA_Si__MO_517338295712_001 | Any | Stillinger-Weber potential for Si optimized for thermal conductivity due to Lee and Hwang (1985); LDA parameterization v001 |
| SW_MX2_KurniawanPetrieWilliams_2021_MoS__MO_677328661525_000 | Any | Modified Stillinger-Weber potential (MX2) for monolayer MoS2 by Kurniawan et al. (2022) v000 |
| SW_MX2_WenShirodkarPlechac_2017_MoS__MO_201919462778_001 | Any | Modified Stillinger-Weber potential (MX2) for monolayer MoS2 developed by Wen et al. (2017) v001 |
| SW_StillingerWeber_1985_Si__MO_405512056662_006 | Any | Stillinger-Weber potential for Si due to Stillinger and Weber (1985) v006 |
| SW_WangStroudMarkworth_1989_CdTe__MO_786496821446_001 | Any | Stillinger-Weber potential for the Cd-Te system developed by Wang, Stroud and Markworth (1989) v001 |
| SW_ZhangXieHu_2014OptimizedSW1_Si__MO_800412945727_005 | Any | Stillinger-Weber potential for Si optimized for silicene developed by Zhang et al. (2014); Parameterization 'Optimized SW1' v005 |
| SW_ZhangXieHu_2014OptimizedSW2_Si__MO_475612090600_005 | Any | Stillinger-Weber potential for Si optimized for silicene developed by Zhang et al. (2014); Parameterization 'Optimized SW2' v005 |
| SW_ZhouWardMartin_2013_CdTeZnSeHgS__MO_503261197030_003 | Any | Stillinger-Weber potential for the Zn-Cd-Hg-S-Se-Te system developed by Zhou et al. (2013) v003 |
| Tersoff_LAMMPS_AlbeNordlundAverback_2002_PtC__MO_500121566391_004 | Any | Tersoff-style three-body potential for PtC developed by Albe, Nordlund, and Averback (2002) v004 |
| Tersoff_LAMMPS_AlbeNordlundNord_2002_GaAs__MO_799020228312_004 | Any | Tersoff-style three-body potential for GaAs developed by Albe et al. (2002) v004 |
| Tersoff_LAMMPS_ByggmastarNagelAlbe_2019_FeO__MO_608695023236_000 | Any | Tersoff-ZBL potential for FeO developed by Byggmastar et al. (2019) v000 |
| Tersoff_LAMMPS_DawLawsonBauschlicher_2011_HfB__MO_328263916986_000 | Any | Tersoff potential for hafnium diboride (HfB_2) developed by Daw et al. (2011) v000 |
| Tersoff_LAMMPS_DawLawsonBauschlicher_2011pot2_ZrB__MO_728716510644_000 | Any | Tersoff potential for zirconium diboride (ZrB2) developed by Daw et al. (2011) v000 |
| Tersoff_LAMMPS_ErhartAlbe_2005_SiC__MO_903987585848_005 | Any | Tersoff-style three-body potential for SiC developed by Erhart and Albe (2005) v005 |
| Tersoff_LAMMPS_ErhartAlbe_2005SiII_SiC__MO_408791041969_004 | Any | Tersoff-style three-body potential for SiC (with SiII parameter set) developed by Erhart and Albe (2005) v004 |
| Tersoff_LAMMPS_ErhartJuslinGoy_2006_ZnO__MO_616776018688_004 | Any | Tersoff-style three-body potential for ZnO developed by Erhart et al. (2006) v004 |
| Tersoff_LAMMPS_KinaciHaskinsSevik_2012_BNC__MO_105008013807_000 | Any | Tersoff-style three-body potential for the B-N-C system developed by Kinaci et al. (2012) v000 |
| Tersoff_LAMMPS_LindsayBroido_2010_C__MO_430669729256_000 | Any | Tersoff-style three-body potential for C modified by Lindsay (2010) v000 |
| Tersoff_LAMMPS_MahdizadehAkhlamadi_2017_Ge__MO_344019981553_000 | Any | Tersoff-style three-body potential for Ge developed by Mahdizadeh and Akhlamadi (2017) v000 |
| Tersoff_LAMMPS_MuellerErhartAlbe_2007_Fe__MO_137964310702_004 | Any | Tersoff-style three-body potential for bcc and fcc Fe developed by Müller, Erhart, and Albe (2007) v004 |
| Tersoff_LAMMPS_MunetohMotookaMoriguchi_2007_SiO__MO_501246546792_000 | Any | Tersoff-style three-body potential for SiO developed by Munetoh et al. (2007) v000 |
| Tersoff_LAMMPS_NordAlbeErhart_2003_GaN__MO_612061685362_004 | Any | Tersoff-style three-body potential for GaN developed by Nord et al. (2003) v004 |
| Tersoff_LAMMPS_PetismeGrenWahnstrom_2015_WCCo__MO_454528624659_000 | Any | Tersoff-style three-body potential for the W-C-Co system developed by Petisme, Gren, and Wahnstrom (2015) v000 |
| Tersoff_LAMMPS_PlummerRathodSrivastava_2021_TiAlC__MO_992900971352_000 | Any | Tersoff-style three-body potential for TiAlC developed by Plummer et al. (2021) v000 |
| Tersoff_LAMMPS_PlummerTucker_2019_TiAlC__MO_736419017411_000 | Any | Tersoff-style three-body potential for TiAlC developed by Plummer and Tucker (2019) v000 |
| Tersoff_LAMMPS_PlummerTucker_2019_TiSiC__MO_751442731010_000 | Any | Tersoff-style three-body potential for TiSiC developed by Plummer and Tucker (2019) v000 |
| Tersoff_LAMMPS_Tersoff_1988_C__MO_579868029681_004 | Any | Tersoff-style three-body potential for C developed by Tersoff (1988) v004 |
| Tersoff_LAMMPS_Tersoff_1988T2_Si__MO_245095684871_004 | Any | Tersoff T2 potential for silicon developed by Tersoff (1988) v004 |
| Tersoff_LAMMPS_Tersoff_1988T3_Si__MO_186459956893_004 | Any | Tersoff T3 potential for silicon developed by Tersoff (1988) v004 |
| Tersoff_LAMMPS_Tersoff_1989_SiC__MO_171585019474_004 | Any | Tersoff-style three-body potential for SiC developed by Tersoff (1989) v004 |
| Tersoff_LAMMPS_Tersoff_1989_SiGe__MO_350526375143_004 | Any | Tersoff-style three-body potential for SiGe developed by Tersoff (1989) v004 |
| Tersoff_LAMMPS_Tersoff_1990_SiC__MO_444207127575_000 | Any | Tersoff-style three-body potential for SiC developed by Tersoff (1990) v000 |
| Tersoff_LAMMPS_Tersoff_1994_SiC__MO_794973922560_000 | Any | Tersoff-style three-body potential for SiC developed by Tersoff (1994) v000 |
| Tersoff_LAMMPS_ZhangNguyen_2021_MoSe__MO_152208847456_001 | Any | Tersoff potentials for large deformation pathways and fracture of MoSe2 v001 |
| ThreeBodyBondOrder_KDS_KhorDasSarma_1988_C__MO_454320668790_000 | Any | Three-body cluster potential for C by Khor and Das Sarma (1988) v000 |
| ThreeBodyBondOrder_KDS_KhorDasSarma_1988_Ge__MO_216597146527_000 | Any | Three-body cluster potential for Ge by Khor and Das Sarma (1988) v000 |
| ThreeBodyBondOrder_KDS_KhorDasSarma_1988_Si__MO_722489435928_000 | Any | Three-body cluster potential for Si by Khor and Das Sarma (1988) v000 |
| ThreeBodyBondOrder_PPM_PurjaPunMishin_2017_Si__MO_566683736730_000 | Any | Three-body bond-order potential for Si by Purja Pun and Mishin (2017) v000 |
| ThreeBodyBondOrder_WR_WangRockett_1991_Si__MO_081872846741_000 | Any | Three-body bond-order potential for Si by Wang and Rockett (1991) v000 |
| ThreeBodyCluster_BH_BiswasHamann_1987_Si__MO_019616213550_000 | Any | Three-body cluster potential for Si by Biswas and Hamann (1987) v000 |
| ThreeBodyCluster_Gong_Gong_1993_Si__MO_407755720412_000 | Any | Three-body cluster potential for Si by Gong (1993) v000 |
| ThreeBodyCluster_KP_KaxirasPandey_1988_Si__MO_072486242437_000 | Any | Three-body cluster potential for Si by Kaxiras and Pandey (1988) v000 |
| ThreeBodyCluster_SRS_StephensonRadnySmith_1996_Si__MO_604248666067_000 | Any | Three-body cluster potential for Si by Stephenson, Radny and Smith (1996) v000 |
| TIDP_RajanWarnerCurtin_2016A_User01__MO_514760222899_001 | Any | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model A, most ductile) v001 |
| TIDP_RajanWarnerCurtin_2016B_User01__MO_217710069583_001 | Any | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model B) v001 |
| TIDP_RajanWarnerCurtin_2016C_User01__MO_072437275969_001 | Any | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model C) v001 |
| TIDP_RajanWarnerCurtin_2016D_User01__MO_791486224463_001 | Any | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model D) v001 |
| TIDP_RajanWarnerCurtin_2016E_User01__MO_971845881377_001 | Any | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model E) v001 |
| TIDP_RajanWarnerCurtin_2016F_User01__MO_246297839798_001 | Any | Tunable Intrinsic Ductility Potential with parameters from Rajan et al. (2016) (Model F, most brittle) v001 |
| TorchML_Allegro_NikidisKyriakopoulosTohid_2024_HCNOS__MO_196060247831_000 | Any | Allegro-EGNN MLIP H,C,N,O,S Pharmaceutical Molecules Developed by Nikidis et al. (2024) v000 |
| TorchML_MACE_BatatiaBennerChiang_2023_MP0a_medium__MO_568776921807_000 | Any | MACE MP 0 'medium' foundation model for atomistic materials chemistry v000 |
| TorchML_MACE_GuptaTadmorMartiniani_2024_Si__MO_781946209112_001 | Any | Parallel MACE Equivariant GNN for Si developed by Gupta et al. (2024) v001 |
| TorchML_NequIP_GuptaTadmorMartiniani_2024_Si__MO_196181738937_001 | Any | Parallel NequIP Equivariant GNN for Si developed by Gupta et al. (2024) v001 |
| TT_Modified_HellmannBichVogel_2007_He__MO_126942667206_002 | Any | Ab initio ground state He+He Interaction potential developed by Hellmann et al. (2007) v002 |