Efficient interatomic descriptors for accurate machine learning force fields of extended molecules

Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations...

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Detalles Bibliográficos
Autores principales: Kabylda, Adil, Vassilev-Galindo, Valentin, Chmiela, Stefan, Poltavsky, Igor, Tkatchenko, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272221/
https://www.ncbi.nlm.nih.gov/pubmed/37322039
http://dx.doi.org/10.1038/s41467-023-39214-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10272221/
https://www.ncbi.nlm.nih.gov/pubmed/37322039
http://dx.doi.org/10.1038/s41467-023-39214-w

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