Machine learning of accurate energy-conserving molecular force fields

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) t...

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Detalles Bibliográficos
Autores principales: Chmiela, Stefan, Tkatchenko, Alexandre, Sauceda, Huziel E., Poltavsky, Igor, Schütt, Kristof T., Müller, Klaus-Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2017
Materias:
Research Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419702/
https://www.ncbi.nlm.nih.gov/pubmed/28508076
http://dx.doi.org/10.1126/sciadv.1603015

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5419702/
https://www.ncbi.nlm.nih.gov/pubmed/28508076
http://dx.doi.org/10.1126/sciadv.1603015

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