Dataset Open Access
Martinetto, Vincent; Shah, Karan; Cangi, Attila; Pribram-Jones, Aurora
<?xml version='1.0' encoding='utf-8'?> <resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"> <identifier identifierType="DOI">10.14278/rodare.2720</identifier> <creators> <creator> <creatorName>Martinetto, Vincent</creatorName> <givenName>Vincent</givenName> <familyName>Martinetto</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6026-7397</nameIdentifier> <affiliation>Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA</affiliation> </creator> <creator> <creatorName>Shah, Karan</creatorName> <givenName>Karan</givenName> <familyName>Shah</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5480-2880</nameIdentifier> <affiliation>Center for Advanced Systems Understanding, 02826 Görlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden</affiliation> </creator> <creator> <creatorName>Cangi, Attila</creatorName> <givenName>Attila</givenName> <familyName>Cangi</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-9162-262X</nameIdentifier> <affiliation>Center for Advanced Systems Understanding, 02826 Görlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden</affiliation> </creator> <creator> <creatorName>Pribram-Jones, Aurora</creatorName> <givenName>Aurora</givenName> <familyName>Pribram-Jones</familyName> <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0244-1814</nameIdentifier> <affiliation>Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA</affiliation> </creator> </creators> <titles> <title>Inverting the Kohn-Sham equations with physics-informed machine learning</title> </titles> <publisher>Rodare</publisher> <publicationYear>2024</publicationYear> <subjects> <subject>density functional theory</subject> <subject>machine learning</subject> </subjects> <dates> <date dateType="Issued">2024-02-01</date> </dates> <resourceType resourceTypeGeneral="Dataset"/> <alternateIdentifiers> <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/2720</alternateIdentifier> </alternateIdentifiers> <relatedIdentifiers> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsSupplementTo">10.48550/arXiv.2312.15301</relatedIdentifier> <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-38725</relatedIdentifier> <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.2719</relatedIdentifier> <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/casus</relatedIdentifier> <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/matter</relatedIdentifier> <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier> <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/zrt</relatedIdentifier> </relatedIdentifiers> <rightsList> <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights> <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights> </rightsList> <descriptions> <description descriptionType="Abstract"><p>This data repository contains the datasets used in the paper &quot;Inverting the Kohn-Sham equations with physics-informed machine learning&quot;.&nbsp;</p> <p>It contains the data generation scripts, datasets for the systems used in the paper (Single Well - 1D atom, Double Well - 1D diatomic molecule) and output potentials&nbsp;generated by the physics-informed machine learning models (physics-informed neural networks and Fourier neural operators).</p></description> </descriptions> </resource>
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