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Revealing ferroelectric switching character using deep recurrent neural networks.
Agar, Joshua C; Naul, Brett; Pandya, Shishir; van der Walt, Stefan; Maher, Joshua; Ren, Yao; Chen, Long-Qing; Kalinin, Sergei V; Vasudevan, Rama K; Cao, Ye; Bloom, Joshua S; Martin, Lane W.
Afiliación
  • Agar JC; Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA. joshua.agar@lehigh.edu.
  • Naul B; Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. joshua.agar@lehigh.edu.
  • Pandya S; Department of Materials Science and Engineering, Lehigh University, Bethlehem, PA, 18015, USA. joshua.agar@lehigh.edu.
  • van der Walt S; Department of Astronomy, University of California, Berkeley, Berkeley, CA, 94720, USA.
  • Maher J; Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA.
  • Ren Y; Berkeley Institute of Data Science, University of California, Berkeley, Berkeley, CA, 94720, USA.
  • Chen LQ; Department of Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, 94720, USA.
  • Kalinin SV; Department of Materials Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA.
  • Vasudevan RK; Department of Materials Science and Engineering, Pennsylvania State University, University Park, PA, 16802-5006, USA.
  • Cao Y; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
  • Bloom JS; Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA.
  • Martin LW; Department of Materials Science and Engineering, The University of Texas at Arlington, Arlington, TX, 76019, USA.
Nat Commun ; 10(1): 4809, 2019 10 22.
Article en En | MEDLINE | ID: mdl-31641122
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2019 Tipo del documento: Article País de afiliación: Estados Unidos