February 1, 2024 Dataset Open Access

Martinetto, Vincent; Shah, Karan; Cangi, Attila; Pribram-Jones, Aurora

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  <dc:creator>Martinetto, Vincent</dc:creator>
  <dc:creator>Shah, Karan</dc:creator>
  <dc:creator>Cangi, Attila</dc:creator>
  <dc:creator>Pribram-Jones, Aurora</dc:creator>
  <dc:date>2024-02-01</dc:date>
  <dc:description>This data repository contains the datasets used in the paper "Inverting the Kohn-Sham equations with physics-informed machine learning". 

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 generated by the physics-informed machine learning models (physics-informed neural networks and Fourier neural operators).</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/2720</dc:identifier>
  <dc:identifier>10.14278/rodare.2720</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:2720</dc:identifier>
  <dc:relation>doi:10.48550/arXiv.2312.15301</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-38725</dc:relation>
  <dc:relation>doi:10.14278/rodare.2719</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/casus</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/matter</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>density functional theory</dc:subject>
  <dc:subject>machine learning</dc:subject>
  <dc:title>Inverting the Kohn-Sham equations with physics-informed machine learning</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>