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Correction: Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage

Joshua Ojih a, Mohammed Al-Fahdi a, Yagang Yao b, Jianjun Hu c and Ming Hu *a
aDepartment of Mechanical Engineering, University of South Carolina, SC 29208, USA. E-mail: hu@sc.edu
bNational Laboratory of Solid State Microstructures, College of Engineering and Applied Sciences, Jiangsu Key Laboratory of Artificial Functional Materials, and Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China
cDepartment of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA

Received 14th June 2024 , Accepted 14th June 2024

First published on 26th June 2024


Abstract

Correction for ‘Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage’ by Joshua Ojih et al., J. Mater. Chem. A, 2024, 12, 8502–8515, https://doi.org/10.1039/D3TA06190F.


On page 8511 in section 3.2 of the published article, the text “The single values of bonding and antibonding for each structure are obtained by performing integration over COHP curves for each atomic pair as evaluated by the LOBSTER package” should have included citations to six references. The missing references are listed below as ref. 1–6.

The Royal Society of Chemistry apologises for these errors and any consequent inconvenience to authors and readers.

References

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