BIGDML—Towards accurate quantum machine learning force fields for materials

Machine-learning force fields (MLFF) should be accurate, computationally and data efficient, and applicable to molecules, materials, and interfaces thereof. Currently, MLFFs often introduce tradeoffs that restrict their practical applicability to small subsets of chemical space or require exhaustive...

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Detalles Bibliográficos
Autores principales: Sauceda, Huziel E., Gálvez-González, Luis E., Chmiela, Stefan, Paz-Borbón, Lauro Oliver, Müller, Klaus-Robert, Tkatchenko, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243122/
https://www.ncbi.nlm.nih.gov/pubmed/35768400
http://dx.doi.org/10.1038/s41467-022-31093-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243122/
https://www.ncbi.nlm.nih.gov/pubmed/35768400
http://dx.doi.org/10.1038/s41467-022-31093-x

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