Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.

Detalles Bibliográficos
Publicado en:Journal of the American Chemical Society Vol. 146; no. 29; pp. 20009 - 20019
Autores principales: Chi Chen, Dan Thien Nguyen, Lee, Shannon J., Baker, Nathan A., Karakoti, Ajay S., Lauw, Linda, Owen, Craig, Mueller, Karl T., Bilodeau, Brian A., Murugesan, Vijayakumar, Troyer, Matthias
Formato: Artículo
Publicado: American Chemical Society 7/24/2024
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=178956197&site=ehost-live
header:
  @attributes:
    shortDbName: hlh
    uiTerm: 178956197
    longDbName: Humanities International Complete
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        00027863
        ACS
      jtl: Journal of the American Chemical Society
      issn: 00027863
      maglogo: N
    pubinfo:
      dt: 7/24/2024
      vid: 146
      iid: 29
      pid: 997
      pub: American Chemical Society
    artinfo:
      ui:
        178956197
        10.1021/jacs.4c03849
      ppf: 20009
      ppct: 10
      formats:
      tig:
        atl: Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.
      aug:
        au:
          Chi Chen
          Dan Thien Nguyen
          Lee, Shannon J.
          Baker, Nathan A.
          Karakoti, Ajay S.
          Lauw, Linda
          Owen, Craig
          Mueller, Karl T.
          Bilodeau, Brian A.
          Murugesan, Vijayakumar
          Troyer, Matthias
        affil:
          Azure Quantum, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
          Pacific Northwest National Laboratory, Physical and Computational Sciences Directorate, 902 Battelle Blvd., Richland, Washington 99352, United States
          Microsoft Surface, Microsoft, One Microsoft Way, Redmond, Washington 98052, United States
      sug:
      pubtype: Academic Journal
      doctype: Article
      src: R
      ab:
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: Y
      dt:
        @attributes:
          year: 2024
    holdings:
      @attributes:
        islocal: N