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

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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Bibliographic Details
Main Authors: Anton Charkin-Gorbulin, Artem Kokorin, Huziel E Sauceda, Stefan Chmiela, Claudio Quarti, David Beljonne, Alexandre Tkatchenko, Igor Poltavsky
Format: Article
Language:English
Published: IOP Publishing 2025-01-01
Series:Machine Learning: Science and Technology
Subjects:
force fields
machine learning
atomistic simulations
molecular dynamics
symmetry search
perovskites
Online Access:https://doi.org/10.1088/2632-2153/adea0b
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https://doi.org/10.1088/2632-2153/adea0b

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