Energy materials screening with defect graph neural networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)
Link
https://www.nature.com/articles/s43588-023-00510-6.pdf
Reference5 articles.
1. McDaniel, A. H. Renewable energy carriers derived from concentrating solar power and nonstoichiometric oxides. Curr. Opin. Green Sustain. 4, 37–43 (2017). A review article offering a perspective on the state of solar thermochemical gas splitting.
2. Goyal, A., Gorai, P., Peng, H., Lany, S. & Stevanovic, V. A computational framework for automation of point defect calculations. Comput. Mater. Sci. 130, 1–9 (2017). This paper describes a high-throughput approach to supercell defect calculations.
3. Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018). This work introduces the Crystal Graph Convolutional Neutral Networks as a machine learning approach for crystalline materials.
4. Lany, S. Communication: the electronic entropy of charged defect formation and its impact on thermochemical redox cycles. J. Chem. Phys. 148, 071101 (2018). This article presents an analysis of the effect of the solid-state reduction entropy on thermochemical gas-phase equilibria.
5. Wexler, R. B., Gautam, G. S., Stechel, E. B. & Carter, E. A. Factors governing oxygen vacancy formation in oxide perovskites. J. Am. Chem. Soc. 143, 13212–13227 (2021). This paper describes linear models to predict oxygen defect formation enthalpies.
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