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Machine learned Hückel theory: Interfacing physics and deep neural networks.
Zubatiuk, Tetiana; Nebgen, Benjamin; Lubbers, Nicholas; Smith, Justin S; Zubatyuk, Roman; Zhou, Guoqing; Koh, Christopher; Barros, Kipton; Isayev, Olexandr; Tretiak, Sergei.
Affiliation
  • Zubatiuk T; Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
  • Nebgen B; Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.
  • Lubbers N; Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
  • Smith JS; Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.
  • Zubatyuk R; Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
  • Zhou G; Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, USA.
  • Koh C; Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.
  • Barros K; Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.
  • Isayev O; Department of Chemistry, Mellon College of Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
  • Tretiak S; Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87544, USA.
J Chem Phys ; 154(24): 244108, 2021 Jun 28.
Article in En | MEDLINE | ID: mdl-34241371

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Qualitative_research Language: En Journal: J Chem Phys Year: 2021 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Qualitative_research Language: En Journal: J Chem Phys Year: 2021 Type: Article Affiliation country: United States