Machine Learning for Molecular Simulation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Noe, Frank | - |
dc.contributor.author | Tkatchenko, Alexandre | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Clementi, Cecilia | - |
dc.date.accessioned | 2021-08-31T16:05:07Z | - |
dc.date.available | 2021-08-31T16:05:07Z | - |
dc.date.created | 2021-06-19 | - |
dc.date.issued | 2020 | - |
dc.identifier.issn | 0066-426X | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/58986 | - |
dc.description.abstract | Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for anML revolution and have already been profoundly affected by the application of existing ML methods. Here we review recent ML methods for molecular simulation, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics. To explain these methods and illustrate open methodological problems, we review some important principles of molecular physics and describe how they can be incorporated into ML structures. Finally, we identify and describe a list of open challenges for the interface between ML and molecular simulation. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ANNUAL REVIEWS | - |
dc.subject | DER-WAALS INTERACTIONS | - |
dc.subject | DYNAMICS SIMULATIONS | - |
dc.subject | NEURAL-NETWORK | - |
dc.subject | FORCE-FIELD | - |
dc.subject | MODELS | - |
dc.subject | KINETICS | - |
dc.subject | BINDING | - |
dc.subject | EXPLORATION | - |
dc.subject | ACCURATE | - |
dc.title | Machine Learning for Molecular Simulation | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1146/annurev-physchem-042018-052331 | - |
dc.identifier.scopusid | 2-s2.0-85076843031 | - |
dc.identifier.wosid | 000530683600016 | - |
dc.identifier.bibliographicCitation | ANNUAL REVIEW OF PHYSICAL CHEMISTRY, VOL 71, v.71, pp.361 - 390 | - |
dc.relation.isPartOf | ANNUAL REVIEW OF PHYSICAL CHEMISTRY, VOL 71 | - |
dc.citation.title | ANNUAL REVIEW OF PHYSICAL CHEMISTRY, VOL 71 | - |
dc.citation.volume | 71 | - |
dc.citation.startPage | 361 | - |
dc.citation.endPage | 390 | - |
dc.type.rims | ART | - |
dc.type.docType | Review; Book Chapter | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Physical | - |
dc.subject.keywordPlus | DER-WAALS INTERACTIONS | - |
dc.subject.keywordPlus | DYNAMICS SIMULATIONS | - |
dc.subject.keywordPlus | NEURAL-NETWORK | - |
dc.subject.keywordPlus | FORCE-FIELD | - |
dc.subject.keywordPlus | MODELS | - |
dc.subject.keywordPlus | KINETICS | - |
dc.subject.keywordPlus | BINDING | - |
dc.subject.keywordPlus | EXPLORATION | - |
dc.subject.keywordPlus | ACCURATE | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | neural networks | - |
dc.subject.keywordAuthor | molecular simulation | - |
dc.subject.keywordAuthor | quantum mechanics | - |
dc.subject.keywordAuthor | coarse graining | - |
dc.subject.keywordAuthor | kinetics | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.