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Hardness and fracture toughness models by symbolic regression

文献类型：期刊论文

作者Zhao, Jinbin2,3; Liu, Peitao2; Wang, Jiantao2,4; Li, Jiangxu2; Niu, Haiyang1; Sun, Yan2; Li, Junlin3; Chen, Xing-Qiu2
刊名EUROPEAN PHYSICAL JOURNAL PLUS
出版日期2023-07-24
卷号138期号:7页码:19
ISSN号2190-5444
DOI10.1140/epjp/s13360-023-04273-x
通讯作者Liu, Peitao(ptliu@imr.ac.cn)
英文摘要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.
资助项目National Key R amp;D Program of China[2021YFB3501503] ; National Natural Science Foundation of China[52201030] ; National Natural Science Foundation of China[52188101] ; National Science Fund for Distinguished Young Scholars[51725103] ; Chinese Academy of Sciences[ZDRW-CN-2021-2-5] ; high performance computational cluster at the Shenyang National University Science and Technology Park
WOS研究方向Physics
语种英语
WOS记录号WOS:001039389600004
出版者SPRINGER HEIDELBERG
资助机构National Key R amp;D Program of China ; National Natural Science Foundation of China ; National Science Fund for Distinguished Young Scholars ; Chinese Academy of Sciences ; high performance computational cluster at the Shenyang National University Science and Technology Park
源URL[http://ir.imr.ac.cn/handle/321006/179008]  
专题金属研究所_中国科学院金属研究所
通讯作者Liu, Peitao
作者单位1.Northwestern Polytech Univ, Int Ctr Mat Discovery, Sch Mat Sci & Engn, State Key Lab Solidificat Proc, Xian 710072, Peoples R China
2.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China
3.Taiyuan Univ Sci & Technol, Sch Mat Sci & Engn, Taiyuan 030024, Peoples R China
4.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Jinbin,Liu, Peitao,Wang, Jiantao,et al. Hardness and fracture toughness models by symbolic regression[J]. EUROPEAN PHYSICAL JOURNAL PLUS,2023,138(7):19.
APA Zhao, Jinbin.,Liu, Peitao.,Wang, Jiantao.,Li, Jiangxu.,Niu, Haiyang.,...&Chen, Xing-Qiu.(2023).Hardness and fracture toughness models by symbolic regression.EUROPEAN PHYSICAL JOURNAL PLUS,138(7),19.
MLA Zhao, Jinbin,et al."Hardness and fracture toughness models by symbolic regression".EUROPEAN PHYSICAL JOURNAL PLUS 138.7(2023):19.

入库方式： OAI收割

来源：金属研究所

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