KOPS - The Institutional Repository of the University of Konstanz

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning

Cite This

Files in this item

Checksum: MD5:a4f5aadc26e95964d795b161524767b4

LANGER, Marcel F., Alex GOESSMANN, Matthias RUPP, 2022. Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning. In: npj Computational Materials. Nature Publishing Group. 8(1), 41. eISSN 2057-3960. Available under: doi: 10.1038/s41524-022-00721-x

@article{Langer2022-12Repre-57047, title={Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning}, year={2022}, doi={10.1038/s41524-022-00721-x}, number={1}, volume={8}, journal={npj Computational Materials}, author={Langer, Marcel F. and Goeßmann, Alex and Rupp, Matthias}, note={Article Number: 41} }

<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/rdf/resource/123456789/57047"> <dcterms:title>Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning</dcterms:title> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-03-29T11:59:27Z</dcterms:available> <dcterms:issued>2022-12</dcterms:issued> <dc:rights>Attribution 4.0 International</dc:rights> <dc:language>eng</dc:language> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/57047"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2022-03-29T11:59:27Z</dc:date> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/57047/1/Langer_2-xuq0viccdgzu0.pdf"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:contributor>Rupp, Matthias</dc:contributor> <dc:contributor>Goeßmann, Alex</dc:contributor> <dc:creator>Langer, Marcel F.</dc:creator> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by/4.0/"/> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dc:creator>Goeßmann, Alex</dc:creator> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/57047/1/Langer_2-xuq0viccdgzu0.pdf"/> <dc:contributor>Langer, Marcel F.</dc:contributor> <dcterms:abstract xml:lang="eng">Computational study of molecules and materials from first principles is a cornerstone of physics, chemistry, and materials science, but limited by the cost of accurate and precise simulations. In settings involving many simulations, machine learning can reduce these costs, often by orders of magnitude, by interpolating between reference simulations. This requires representations that describe any molecule or material and support interpolation. We comprehensively review and discuss current representations and relations between them. For selected state-of-the-art representations, we compare energy predictions for organic molecules, binary alloys, and Al–Ga–In sesquioxides in numerical experiments controlled for data distribution, regression method, and hyper-parameter optimization.</dcterms:abstract> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:creator>Rupp, Matthias</dc:creator> </rdf:Description> </rdf:RDF>

Downloads since Mar 29, 2022 (Information about access statistics)

Langer_2-xuq0viccdgzu0.pdf 133

This item appears in the following Collection(s)

Attribution 4.0 International Except where otherwise noted, this item's license is described as Attribution 4.0 International

Search KOPS


Browse

My Account