Finding Density Functionals with Machine Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Snyder, John C. | - |
dc.contributor.author | Rupp, Matthias | - |
dc.contributor.author | Hansen, Katja | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Burke, Kieron | - |
dc.date.accessioned | 2021-09-06T18:40:08Z | - |
dc.date.available | 2021-09-06T18:40:08Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2012-06-19 | - |
dc.identifier.issn | 0031-9007 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/108147 | - |
dc.description.abstract | Machine learning is used to approximate density functionals. For the model problem of the kinetic energy of noninteracting fermions in 1D, mean absolute errors below 1 kcal/mol on test densities similar to the training set are reached with fewer than 100 training densities. A predictor identifies if a test density is within the interpolation region. Via principal component analysis, a projected functional derivative finds highly accurate self-consistent densities. The challenges for application of our method to real electronic structure problems are discussed. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | AMER PHYSICAL SOC | - |
dc.subject | APPROXIMATION | - |
dc.title | Finding Density Functionals with Machine Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1103/PhysRevLett.108.253002 | - |
dc.identifier.scopusid | 2-s2.0-84862560607 | - |
dc.identifier.wosid | 000305568700008 | - |
dc.identifier.bibliographicCitation | PHYSICAL REVIEW LETTERS, v.108, no.25 | - |
dc.relation.isPartOf | PHYSICAL REVIEW LETTERS | - |
dc.citation.title | PHYSICAL REVIEW LETTERS | - |
dc.citation.volume | 108 | - |
dc.citation.number | 25 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Physics, Multidisciplinary | - |
dc.subject.keywordPlus | APPROXIMATION | - |
dc.subject.keywordAuthor | Functional derivatives | - |
dc.subject.keywordAuthor | Mean absolute error | - |
dc.subject.keywordAuthor | Model problems | - |
dc.subject.keywordAuthor | Noninteracting fermions | - |
dc.subject.keywordAuthor | Training sets | - |
dc.subject.keywordAuthor | Electronic structure | - |
dc.subject.keywordAuthor | Principal component analysis | - |
dc.subject.keywordAuthor | Learning systems | - |
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