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Closed-loop optimization of fast-charging protocols for batteries with machine learning.
Attia, Peter M; Grover, Aditya; Jin, Norman; Severson, Kristen A; Markov, Todor M; Liao, Yang-Hung; Chen, Michael H; Cheong, Bryan; Perkins, Nicholas; Yang, Zi; Herring, Patrick K; Aykol, Muratahan; Harris, Stephen J; Braatz, Richard D; Ermon, Stefano; Chueh, William C.
Afiliación
  • Attia PM; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Grover A; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Jin N; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Severson KA; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Markov TM; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Liao YH; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Chen MH; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Cheong B; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Perkins N; Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Yang Z; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Herring PK; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Aykol M; Toyota Research Institute, Los Altos, CA, USA.
  • Harris SJ; Toyota Research Institute, Los Altos, CA, USA.
  • Braatz RD; Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
  • Ermon S; Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
  • Chueh WC; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. braatz@mit.edu.
Nature ; 578(7795): 397-402, 2020 02.
Article en En | MEDLINE | ID: mdl-32076218
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nature Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Nature Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido