Correction: Accelerating materials discovery using integrated deep machine learning approaches

Journal Article · 19 December 2023 · Journal of Materials Chemistry. A

DOI: https://doi.org/10.1039/D3TA90266H · OSTI ID:2228774

Xia, Weiyi ^[1]; Tang, Ling ^[2]; Sun, Huaijun ^[3]; Zhang, Chao ^[4]; Ho, Kai-Ming ^[5]; Viswanathan, Gayatri ^[6]; Kovnir, Kirill ^[6]; Wang, Cai-Zhuang ^[7]

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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