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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
Kovnir
af and
Cai-Zhuang
Wang
*ae
aAmes National Laboratory, U.S. Department of Energy, Ames, IA 50011, USA
bDepartment of Applied Physics, College of Science, Zhejiang University of Technology, Hangzhou, 310023, China
cJiyang College of Zhejiang Agriculture, Forestry University, Zhuji 311800, China
dDepartment of Physics, Yantai University, Yantai 264005, China
eDepartment of Physics and Astronomy, Iowa State University, Ames, IA 50011, USA
fDepartment of Chemistry, Iowa State University, Ames, IA 50011, USA
First published on 6th December 2023
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.
The authors apologise for an error in Fig. 5c and d. The figure previously mistakenly portrayed the electronic band structure and electronic density of states, rather than the described phonon dispersion and density of states. The corrected figure is shown below.
 | ||
| Fig. 5 (a and b) The structures of the predicted two La2SiP3 phases. The formation energies above the convex hull are 1 meV per atom and 33 meV per atom, respectively. (c and d) The phonon dispersion and density of states of the two predicted La2SiP3 phases. | ||
The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.
| This journal is © The Royal Society of Chemistry 2024 |