https://doi.org/10.1140/epjp/s13360-023-04273-x
Regular Article
Hardness and fracture toughness models by symbolic regression
1
Shenyang National Laboratory for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, 110016, Shenyang, China
2
School of Materials Science and Engineering, Taiyuan University of Science and Technology, 030024, Taiyuan, China
3
School of Materials Science and Engineering, University of Science and Technology of China, 110016, Shenyang, China
4
State Key Laboratory of Solidification Processing, International Center for Materials Discovery, School of Materials Science and Engineering, NorthwesternPolytechnical University, 710072, Xi’an, People’s Republic of China
Received:
2
June
2023
Accepted:
11
July
2023
Published online:
24
July
2023
Superhard materials with good fracture toughness have found wide industrial applications, which necessitates the development of accurate hardness and fracture toughness models for efficient materials design. Although several macroscopic models have been proposed, they are mostly semiempirical based on prior knowledge or assumptions, and obtained by fitting limited experimental data. Here, through an unbiased and explanatory symbolic regression technique, we built a macroscopic hardness model and fracture toughness model, which only require shear and bulk moduli as inputs. The developed hardness model was trained on an extended dataset including more non-cubic systems. The obtained models turned out to be simple, accurate, and transferable. Moreover, we assessed the performance of three popular deep learning models for predicting bulk and shear moduli, and found that the crystal graph convolutional neural network and crystal explainable property predictor perform almost equally well, both better than the atomistic line graph neural network. By combining the machine-learned bulk and shear moduli with the hardness and fracture toughness prediction models, potential superhard materials with good fracture toughness can be efficiently screened out through high-throughput calculations.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
