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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...
Αποθηκεύτηκε σε:
| Τόπος έκδοσης: | Sci Adv |
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| Κύριοι συγγραφείς: | , , , , , |
| Μορφή: | Artigo |
| Γλώσσα: | Inglês |
| Έκδοση: |
American Association for the Advancement of Science
2017
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| Θέματα: | |
| Διαθέσιμο Online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5419702/ https://ncbi.nlm.nih.gov/pubmed/28508076 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1126/sciadv.1603015 |
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