Atomic orbits in molecules and materials for improving machine learning force fields

The accurate representation of atoms within their environment forms the backbone of any reliable machine learning force field (MLFF). While modern MLFFs treat atoms of the same type as indistinguishable, their identities can be further resolved by accounting for the composition of their chemical env...

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Autors principals:	Anton Charkin-Gorbulin, Artem Kokorin, Huziel E Sauceda, Stefan Chmiela, Claudio Quarti, David Beljonne, Alexandre Tkatchenko, Igor Poltavsky
Format:	Artigo
Idioma:	Inglês
Publicat:	IOP Publishing 2025-01-01
Col·lecció:	Machine Learning: Science and Technology
Matèries:	force fields machine learning atomistic simulations molecular dynamics symmetry search perovskites
Accés en línia:	https://doi.org/10.1088/2632-2153/adea0b
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