Publikation: Optimizing transition states via kernel-based machine learning
Lade...
Dateien
Zu diesem Dokument gibt es keine Dateien.
Datum
2012
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
DOI (zitierfähiger Link)
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
The Journal of Chemical Physics. American Institute of Physics (AIP). 2012, 136(17), 174101. ISSN 0021-9606. eISSN 1089-7690. Available under: doi: 10.1063/1.4707167
Zusammenfassung
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.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
004 Informatik
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Zitieren
ISO 690
POZUN, Zachary D., Katja HANSEN, Daniel SHEPPARD, Matthias RUPP, Klaus-Robert MÜLLER, Graeme HENKELMAN, 2012. Optimizing transition states via kernel-based machine learning. In: The Journal of Chemical Physics. American Institute of Physics (AIP). 2012, 136(17), 174101. ISSN 0021-9606. eISSN 1089-7690. Available under: doi: 10.1063/1.4707167BibTex
@article{Pozun2012-05-07Optim-52494,
year={2012},
doi={10.1063/1.4707167},
title={Optimizing transition states via kernel-based machine learning},
number={17},
volume={136},
issn={0021-9606},
journal={The Journal of Chemical Physics},
author={Pozun, Zachary D. and Hansen, Katja and Sheppard, Daniel and Rupp, Matthias and Müller, Klaus-Robert and Henkelman, Graeme},
note={Article Number: 174101}
}RDF
<rdf:RDF
xmlns:dcterms="http://purl.org/dc/terms/"
xmlns:dc="http://purl.org/dc/elements/1.1/"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:bibo="http://purl.org/ontology/bibo/"
xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
xmlns:foaf="http://xmlns.com/foaf/0.1/"
xmlns:void="http://rdfs.org/ns/void#"
xmlns:xsd="http://www.w3.org/2001/XMLSchema#" >
<rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/52494">
<dc:creator>Sheppard, Daniel</dc:creator>
<void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
<dcterms:title>Optimizing transition states via kernel-based machine learning</dcterms:title>
<dc:creator>Henkelman, Graeme</dc:creator>
<dc:creator>Müller, Klaus-Robert</dc:creator>
<dc:contributor>Pozun, Zachary D.</dc:contributor>
<dc:contributor>Rupp, Matthias</dc:contributor>
<dc:contributor>Sheppard, Daniel</dc:contributor>
<dc:creator>Rupp, Matthias</dc:creator>
<dc:language>eng</dc:language>
<dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-01-18T12:39:16Z</dcterms:available>
<dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
<dc:contributor>Hansen, Katja</dc:contributor>
<bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/52494"/>
<dc:rights>terms-of-use</dc:rights>
<dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
<dc:creator>Pozun, Zachary D.</dc:creator>
<dc:contributor>Müller, Klaus-Robert</dc:contributor>
<dc:contributor>Henkelman, Graeme</dc:contributor>
<foaf:homepage rdf:resource="http://localhost:8080/"/>
<dc:creator>Hansen, Katja</dc:creator>
<dcterms:issued>2012-05-07</dcterms:issued>
<dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-01-18T12:39:16Z</dc:date>
<dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
<dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
</rdf:Description>
</rdf:RDF>Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Ja
