Bypassing the Kohn-Sham equations with machine learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Brockherde, Felix | - |
dc.contributor.author | Vogt, Leslie | - |
dc.contributor.author | Li, Li | - |
dc.contributor.author | Tuckerman, Mark E. | - |
dc.contributor.author | Burke, Kieron | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.date.accessioned | 2021-09-03T00:11:14Z | - |
dc.date.available | 2021-09-03T00:11:14Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2017-10-11 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/81917 | - |
dc.description.abstract | Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | NATURE PUBLISHING GROUP | - |
dc.subject | DENSITY | - |
dc.subject | PSEUDOPOTENTIALS | - |
dc.subject | ENERGY | - |
dc.subject | APPROXIMATION | - |
dc.subject | SPACE | - |
dc.subject | DFT | - |
dc.title | Bypassing the Kohn-Sham equations with machine learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1038/s41467-017-00839-3 | - |
dc.identifier.scopusid | 2-s2.0-85031128428 | - |
dc.identifier.wosid | 000412776200009 | - |
dc.identifier.bibliographicCitation | NATURE COMMUNICATIONS, v.8 | - |
dc.relation.isPartOf | NATURE COMMUNICATIONS | - |
dc.citation.title | NATURE COMMUNICATIONS | - |
dc.citation.volume | 8 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
dc.subject.keywordPlus | DENSITY | - |
dc.subject.keywordPlus | PSEUDOPOTENTIALS | - |
dc.subject.keywordPlus | ENERGY | - |
dc.subject.keywordPlus | APPROXIMATION | - |
dc.subject.keywordPlus | SPACE | - |
dc.subject.keywordPlus | DFT | - |
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