Optimizing transition states via kernel-based machine learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Pozun, Zachary D. | - |
dc.contributor.author | Hansen, Katja | - |
dc.contributor.author | Sheppard, Daniel | - |
dc.contributor.author | Rupp, Matthias | - |
dc.contributor.author | Mueller, Klaus-Robert | - |
dc.contributor.author | Henkelman, Graeme | - |
dc.date.accessioned | 2021-09-06T19:55:41Z | - |
dc.date.available | 2021-09-06T19:55:41Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2012-05-07 | - |
dc.identifier.issn | 0021-9606 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/108441 | - |
dc.description.abstract | We present a method for optimizing transition state theory dividing surfaces with support vector machines. The resulting dividing surfaces require no a priori information or intuition about reaction mechanisms. To generate optimal dividing surfaces, we apply a cycle of machine-learning and refinement of the surface by molecular dynamics sampling. We demonstrate that the machine-learned surfaces contain the relevant low-energy saddle points. The mechanisms of reactions may be extracted from the machine-learned surfaces in order to identify unexpected chemically relevant processes. Furthermore, we show that the machine-learned surfaces significantly increase the transmission coefficient for an adatom exchange involving many coupled degrees of freedom on a (100) surface when compared to a distance-based dividing surface. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4707167] | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | AMER INST PHYSICS | - |
dc.subject | MOLECULAR-DYNAMICS | - |
dc.subject | SURFACE | - |
dc.title | Optimizing transition states via kernel-based machine learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Mueller, Klaus-Robert | - |
dc.identifier.doi | 10.1063/1.4707167 | - |
dc.identifier.scopusid | 2-s2.0-84862891798 | - |
dc.identifier.wosid | 000303935700002 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CHEMICAL PHYSICS, v.136, no.17 | - |
dc.relation.isPartOf | JOURNAL OF CHEMICAL PHYSICS | - |
dc.citation.title | JOURNAL OF CHEMICAL PHYSICS | - |
dc.citation.volume | 136 | - |
dc.citation.number | 17 | - |
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 | MOLECULAR-DYNAMICS | - |
dc.subject.keywordPlus | SURFACE | - |
dc.subject.keywordAuthor | Distance-based | - |
dc.subject.keywordAuthor | Dividing surfaces | - |
dc.subject.keywordAuthor | Low energies | - |
dc.subject.keywordAuthor | Machine-learning | - |
dc.subject.keywordAuthor | Priori information | - |
dc.subject.keywordAuthor | Reaction mechanism | - |
dc.subject.keywordAuthor | Saddle point | - |
dc.subject.keywordAuthor | Transition state | - |
dc.subject.keywordAuthor | Transition state theories | - |
dc.subject.keywordAuthor | Transmission coefficients | - |
dc.subject.keywordAuthor | Learning systems | - |
dc.subject.keywordAuthor | Molecular dynamics | - |
dc.subject.keywordAuthor | Reaction kinetics | - |
dc.subject.keywordAuthor | Optimization | - |
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