Issue 1, 2024
From the journal:

Journal of Materials Chemistry A

Correction: Accelerating materials discovery using integrated deep machine learning approaches

Weiyi Xia,a   Ling Tang,b   Huaijun Sun,c   Chao Zhang,d   Kai-Ming Ho,e   Gayatri Viswanathan,af   Kirill Kovniraf  and  Cai-Zhuang Wang*ae  

* Corresponding authors

a Ames National Laboratory, U.S. Department of Energy, Ames, IA 50011, USA

b Department of Applied Physics, College of Science, Zhejiang University of Technology, Hangzhou, China

c Jiyang College of Zhejiang Agriculture, Forestry University, Zhuji 311800, China

d Department of Physics, Yantai University, Yantai 264005, China

e Department of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA

f Department of Chemistry, Iowa State University, Ames, IA 50011, USA

Abstract

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.

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Article information

DOI
https://doi.org/10.1039/D3TA90266H
Article type
Correction
Submitted
28 Nov 2023
Accepted
28 Nov 2023
First published
06 Dec 2023
This article is Open Access
Creative Commons BY license

J. Mater. Chem. A, 2024,12, 521-521

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Correction: Accelerating materials discovery using integrated deep machine learning approaches

W. Xia, L. Tang, H. Sun, C. Zhang, K. Ho, G. Viswanathan, K. Kovnir and C. Wang, J. Mater. Chem. A, 2024, 12, 521 DOI: 10.1039/D3TA90266H

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