Publikation:

Machine Learning for Quantum Mechanical Properties of Atoms in Molecules

Vorschaubild

Dateien

Zu diesem Dokument gibt es keine Dateien.

Datum

2015

Autor:innen

Ramakrishnan, Raghunathan
von Lilienfeld, O. Anatole

Herausgeber:innen

Kontakt

ISSN der Zeitschrift

Electronic ISSN

ISBN

Bibliografische Daten

Verlag

Schriftenreihe

Auflagebezeichnung

URI (zitierfähiger Link)
DOI (zitierfähiger Link)
https://doi.org/10.1021/acs.jpclett.5b01456
ArXiv-ID
http://arxiv.org/abs/1505.00350v2

Internationale Patentnummer

Angaben zur Forschungsförderung

Projekt

Open Access-Veröffentlichung

Sammlungen

Informatik und Informationswissenschaft: Publikationen
Core Facility der Universität Konstanz

Gesperrt bis

Titel in einer weiteren Sprache

Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published

Erschienen in

Journal of Physical Chemistry Letters. American Chemical Society (ACS). 2015, 6(16), pp. 3309-3313. eISSN 1948-7185. Available under: doi: 10.1021/acs.jpclett.5b01456

Zusammenfassung

We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach accuracies on par with density functional theory reference. Locality is exploited within nonlinear regression via local atom-centered coordinate systems. The approach is validated on a diverse set of 9 k small organic molecules. Linear scaling of computational cost in system size is demonstrated for saturated polymers with up to submesoscale lengths.

Zusammenfassung in einer weiteren Sprache

Fachgebiet (DDC)
004 Informatik

Schlagwörter

machine learning, chemical shifts, core level ionization energies, forces, density functional theory, kernel ridge regression, linear scaling

Konferenz

Rezension
undefined / . - undefined, undefined

Forschungsvorhaben

Organisationseinheiten

Zeitschriftenheft

Zugehörige Datensätze in KOPS

Zitieren

ISO 690RUPP, Matthias, Raghunathan RAMAKRISHNAN, O. Anatole VON LILIENFELD, 2015. Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. In: Journal of Physical Chemistry Letters. American Chemical Society (ACS). 2015, 6(16), pp. 3309-3313. eISSN 1948-7185. Available under: doi: 10.1021/acs.jpclett.5b01456
BibTex
@article{Rupp2015-05-02T16:11:05ZMachi-52125,
  year={2015},
  doi={10.1021/acs.jpclett.5b01456},
  title={Machine Learning for Quantum Mechanical Properties of Atoms in Molecules},
  number={16},
  volume={6},
  journal={Journal of Physical Chemistry Letters},
  pages={3309--3313},
  author={Rupp, Matthias and Ramakrishnan, Raghunathan and von Lilienfeld, O. Anatole}
}
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/123456789/52125">
    <dc:language>eng</dc:language>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <dc:contributor>Rupp, Matthias</dc:contributor>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:creator>von Lilienfeld, O. Anatole</dc:creator>
    <dc:contributor>von Lilienfeld, O. Anatole</dc:contributor>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-12-15T12:16:55Z</dc:date>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/>
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Ramakrishnan, Raghunathan</dc:creator>
    <dcterms:abstract xml:lang="eng">We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach accuracies on par with density functional theory reference. Locality is exploited within nonlinear regression via local atom-centered coordinate systems. The approach is validated on a diverse set of 9 k small organic molecules. Linear scaling of computational cost in system size is demonstrated for saturated polymers with up to submesoscale lengths.</dcterms:abstract>
    <dcterms:issued>2015-05-02T16:11:05Z</dcterms:issued>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-12-15T12:16:55Z</dcterms:available>
    <dc:contributor>Ramakrishnan, Raghunathan</dc:contributor>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dc:creator>Rupp, Matthias</dc:creator>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/52125"/>
    <dcterms:title>Machine Learning for Quantum Mechanical Properties of Atoms in Molecules</dcterms:title>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
  </rdf:Description>
</rdf:RDF>

Interner Vermerk

xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter

Kontakt
URL der Originalveröffentl.

Prüfdatum der URL

Prüfungsdatum der Dissertation

Finanzierungsart

Kommentar zur Publikation

Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Nein
Begutachtet
Ja
Diese Publikation teilen