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Energy materials screening with defect graph neural networks

Graph neural networks (GNNs) present a promising route for machine learning of solid-state materials’ properties, but methods capable of directly predicting defect properties from ideal, defect-free structures are needed. A GNN developed for direct defect property predictions enables high-throughput screening of redox-active oxides for energy applications and beyond.

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Fig. 1: Computational workflow and statistical analysis of the ML model.


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This is a summary of: Witman, M. D. et al. Defect graph neural networks for materials discovery in high-temperature clean-energy applications. Nat. Comput. Sci. (2023).

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Energy materials screening with defect graph neural networks. Nat Comput Sci 3, 671–672 (2023).

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