Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
DC Field | Value | Language |
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dc.contributor.author | Hansen, Katja | - |
dc.contributor.author | Montavon, Gregoire | - |
dc.contributor.author | Biegler, Franziska | - |
dc.contributor.author | Fazli, Siamac | - |
dc.contributor.author | Rupp, Matthias | - |
dc.contributor.author | Scheffler, Matthias | - |
dc.contributor.author | von Lilienfeld, O. Anatole | - |
dc.contributor.author | Tkatchenko, Alexandre | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.date.accessioned | 2021-09-05T23:16:18Z | - |
dc.date.available | 2021-09-05T23:16:18Z | - |
dc.date.created | 2021-06-14 | - |
dc.date.issued | 2013-08 | - |
dc.identifier.issn | 1549-9618 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/102577 | - |
dc.description.abstract | The accurate and reliable prediction of properties of molecules typically requires computationally intensive quantum-chemical calculations. Recently, machine learning techniques applied to ab initio calculations have been proposed as an efficient approach for describing the energies of molecules in their given ground-state structure throughout chemical compound space (Rupp et al. Phys. Rev. Lett. 2012, 108, 058301). In this paper we outline a number of established machine learning techniques and investigate the influence of the molecular representation on the methods performance. The best methods achieve prediction errors of 3 kcal/mol for the atomization energies of a wide variety of molecules. Rationales for this performance improvement are given together with pitfalls and challenges when applying machine learning approaches to the prediction of quantum-mechanical observables. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | AMER CHEMICAL SOC | - |
dc.subject | MIXED-EFFECTS MODELS | - |
dc.subject | DEEP | - |
dc.subject | SURFACES | - |
dc.subject | REGRESSION | - |
dc.subject | SELECTION | - |
dc.subject | BIAS | - |
dc.title | Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Fazli, Siamac | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1021/ct400195d | - |
dc.identifier.scopusid | 2-s2.0-84882415695 | - |
dc.identifier.wosid | 000323193500015 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CHEMICAL THEORY AND COMPUTATION, v.9, no.8, pp.3404 - 3419 | - |
dc.relation.isPartOf | JOURNAL OF CHEMICAL THEORY AND COMPUTATION | - |
dc.citation.title | JOURNAL OF CHEMICAL THEORY AND COMPUTATION | - |
dc.citation.volume | 9 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 3404 | - |
dc.citation.endPage | 3419 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Physical | - |
dc.relation.journalWebOfScienceCategory | Physics, Atomic, Molecular & Chemical | - |
dc.subject.keywordPlus | MIXED-EFFECTS MODELS | - |
dc.subject.keywordPlus | DEEP | - |
dc.subject.keywordPlus | SURFACES | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | BIAS | - |
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