Correction: Graph Theory and Graph Neural Network Assisted High-Throughput Crystal Structure Prediction and Screening for Energy Conversion and Storage

Author(s)

Joshua Ojih
Mohammed Al-Fahdi
Yagang Yao
Jianjun Hu, University of South CarolinaFollow
Ming Hu, University of South CarolinaFollow

Document Type

Article

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.

Digital Object Identifier (DOI)

https://doi.org/10.1039/D4TA90115K

Publication Info

Published in Journal of Materials Chemistry A, Volume 12, 2024, pages 16929-.

Rights

© The Royal Society of Chemistry 2024 This article is licensed under a Creative Commons Attribution 3.0 Unported Licence

APA Citation

Ojih, J., Al-Fahdi, M., Yao, Y., Hu, J., & Hu, M. (2024). Correction: Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage. Journal of Materials Chemistry A, 12(27), 16929.https://doi.org/10.1039/D4TA90115K