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- W3154448265 abstract "Machine learning (ML) models utilizing structure-based features provide an efficient means for accurate property predictions across diverse chemical spaces. However, obtaining equilibrium crystal structures typically requires expensive density functional theory (DFT) calculations, which limits ML-based exploration to either known crystals or a small number of hypothetical crystals. Here, we demonstrate that the application of Bayesian optimization with symmetry constraints using a graph deep learning energy model can be used to perform DFT-free relaxations of crystal structures. Using this approach to significantly improve the accuracy of ML-predicted formation energies and elastic moduli of hypothetical crystals, two novel ultra-incompressible hard materials MoWC2 (P63/mmc) and ReWB (Pca21) were identified and successfully synthesized via in-situ reactive spark plasma sintering from a screening of 399,960 transition metal borides and carbides. This work addresses a critical bottleneck to accurate property predictions for hypothetical materials, paving the way to ML-accelerated discovery of new materials with exceptional properties." @default.
- W3154448265 created "2021-04-26" @default.
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- W3154448265 date "2021-04-20" @default.
- W3154448265 modified "2023-09-25" @default.
- W3154448265 title "Accelerating Materials Discovery with Bayesian Optimization and Graph Deep Learning" @default.
- W3154448265 cites W1964646921 @default.
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- W3154448265 doi "https://doi.org/10.48550/arxiv.2104.10242" @default.
- W3154448265 hasPublicationYear "2021" @default.
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