Open AccessOptimized symmetry functions for machine-learning interatomic potentials of multicomponent systems
Author(s) -
Samare Rostami,
Maximilian Amsler,
S. Alireza Ghasemi
Publication year - 2018
Publication title -
the journal of chemical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.071
H-Index - 357
eISSN - 1089-7690
pISSN - 0021-9606
DOI - 10.1063/1.5040005
Subject(s) - symmetry (geometry) , artificial neural network , scaling , ab initio , ionic bonding , yield (engineering) , work (physics) , statistical physics , ab initio quantum chemistry methods , energy (signal processing) , computer science , materials science , biological system , chemical physics , chemistry , physics , machine learning , thermodynamics , molecule , mathematics , quantum mechanics , ion , geometry , biology
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