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Recent advances in machine learning (ML) methods have led to substantial improvement in materials property prediction against community benchmarks, but an excellent benchmark score may not imply good generalization of performance.
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T. Xie and J. C. Grossman, Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties, Phys. Rev. Lett. 120
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2021
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2021
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2021
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2021
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M. L. Green, B. Maruyama, and J. Schrier, Autonomous (ai-driven) materials science, Applied Physics Reviews 9
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