Quantum chemical accuracy from density functional approximations via machine learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bogojeski, Mihail | - |
dc.contributor.author | Vogt-Maranto, Leslie | - |
dc.contributor.author | Tuckerman, Mark E. | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Burke, Kieron | - |
dc.date.accessioned | 2021-08-30T10:50:52Z | - |
dc.date.available | 2021-08-30T10:50:52Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020-10-16 | - |
dc.identifier.issn | 2041-1723 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/52435 | - |
dc.description.abstract | Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal . mol(-1) with presently-available functionals. Ab initio methods, such as coupled-cluster, routinely produce much higher accuracy, but computational costs limit their application to small molecules. In this paper, we leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching quantum chemical accuracy (errors below 1 kcal . mol(-1)) on test data. Moreover, density-based Delta -learning (learning only the correction to a standard DFT calculation, termed Delta -DFT ) significantly reduces the amount of training data required, particularly when molecular symmetries are included. The robustness of Delta -DFT is highlighted by correcting "on the fly" DFT-based molecular dynamics (MD) simulations of resorcinol (C6H4(OH)(2)) to obtain MD trajectories with coupled-cluster accuracy. We conclude, therefore, that Delta -DFT facilitates running gas-phase MD simulations with quantum chemical accuracy, even for strained geometries and conformer changes where standard DFT fails. High-level ab initio quantum chemical methods carry a high computational burden, thus limiting their applicability. Here, the authors employ machine learning to generate coupled-cluster energies and forces at chemical accuracy for geometry optimization and molecular dynamics from DFT densities. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | NATURE RESEARCH | - |
dc.subject | MOLECULAR-DYNAMICS | - |
dc.subject | PSEUDOPOTENTIALS | - |
dc.subject | PROTON | - |
dc.subject | POTENTIALS | - |
dc.subject | PROGRAM | - |
dc.subject | MODELS | - |
dc.subject | ATOMS | - |
dc.title | Quantum chemical accuracy from density functional approximations via machine learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1038/s41467-020-19093-1 | - |
dc.identifier.scopusid | 2-s2.0-85092626669 | - |
dc.identifier.wosid | 000582054700002 | - |
dc.identifier.bibliographicCitation | NATURE COMMUNICATIONS, v.11, no.1 | - |
dc.relation.isPartOf | NATURE COMMUNICATIONS | - |
dc.citation.title | NATURE COMMUNICATIONS | - |
dc.citation.volume | 11 | - |
dc.citation.number | 1 | - |
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 | MOLECULAR-DYNAMICS | - |
dc.subject.keywordPlus | PSEUDOPOTENTIALS | - |
dc.subject.keywordPlus | PROTON | - |
dc.subject.keywordPlus | POTENTIALS | - |
dc.subject.keywordPlus | PROGRAM | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | ATOMS | - |
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