Dataset Open Access
Martinetto, Vincent;
Shah, Karan;
Cangi, Attila;
Pribram-Jones, Aurora
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<foaf:name>Martinetto, Vincent</foaf:name>
<foaf:givenName>Vincent</foaf:givenName>
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<foaf:name>Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA</foaf:name>
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<foaf:name>Center for Advanced Systems Understanding, 02826 Görlitz, Germany/Helmholtz-Zentrum Dresden-Rossendorf, Bautzner Landstraße 400, 01328 Dresden</foaf:name>
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<foaf:name>Pribram-Jones, Aurora</foaf:name>
<foaf:givenName>Aurora</foaf:givenName>
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<foaf:name>Department of Chemistry and Biochemistry, University of California Merced, 5200 North Lake Rd., Merced, California 95343, USA</foaf:name>
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<dct:title>Inverting the Kohn-Sham equations with physics-informed machine learning</dct:title>
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<foaf:name>Rodare</foaf:name>
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<dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#gYear">2024</dct:issued>
<dcat:keyword>density functional theory</dcat:keyword>
<dcat:keyword>machine learning</dcat:keyword>
<dct:issued rdf:datatype="http://www.w3.org/2001/XMLSchema#date">2024-02-01</dct:issued>
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<dct:description><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></dct:description>
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