1. Accelerating materials discovery with Bayesian optimization and graph deep learning.
- Author
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Zuo, Yunxing, Qin, Mingde, Chen, Chi, Ye, Weike, Li, Xiangguo, Luo, Jian, and Ong, Shyue Ping
- Subjects
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DEEP learning , *TRANSITION metal carbides , *DENSITY functional theory , *CRYSTAL structure , *MACHINE learning , *ELASTIC modulus - Abstract
[Display omitted] 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 MoWC 2 (P 6 3 / mmc) and ReWB (Pca 2 1 ) were identified and successfully synthesized via in situ reactive spark plasma sintering from screening 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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