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Correction: Accelerating materials discovery using integrated deep machine learning approaches

Journal Article · · Journal of Materials Chemistry. A
DOI:https://doi.org/10.1039/D3TA90266H· OSTI ID:2228774
 [1];  [2];  [3];  [4];  [5];  [6];  [6];  [7]
  1. Ames National Laboratory, U.S. Department of Energy, Ames, IA 50011, USA
  2. Department of Applied Physics, College of Science, Zhejiang University of Technology, Hangzhou, 310023, China
  3. Jiyang College of Zhejiang Agriculture, Forestry University, Zhuji 311800, China
  4. Department of Physics, Yantai University, Yantai 264005, China
  5. Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA
  6. Ames National Laboratory, U.S. Department of Energy, Ames, IA 50011, USA, Department of Chemistry, Iowa State University, Ames, IA 50011, USA
  7. Ames National Laboratory, U.S. Department of Energy, Ames, IA 50011, USA, Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA

Correction for ‘Accelerating materials discovery using integrated deep machine learning approaches’ by Weiyi Xia et al. , J. Mater. Chem. A , 2023, 11 , 25973–25982, https://doi.org/10.1039/d3ta03771a.

Sponsoring Organization:
USDOE
Grant/Contract Number:
AC02-07CH11358
OSTI ID:
2228774
Journal Information:
Journal of Materials Chemistry. A, Journal Name: Journal of Materials Chemistry. A Journal Issue: 1 Vol. 12; ISSN JMCAET; ISSN 2050-7488
Publisher:
Royal Society of Chemistry (RSC)Copyright Statement
Country of Publication:
United Kingdom
Language:
English

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