Hardness and fracture toughness models by symbolic regression

doi:10.1140/epjp/s13360-023-04273-x

IMR OpenIR

	Hardness and fracture toughness models by symbolic regression
	Zhao, Jinbin 1,2; Liu, Peitao 1; Wang, Jiantao 1,3; Li, Jiangxu 1; Niu, Haiyang 4; Sun, Yan 1; Li, Junlin 2; Chen, Xing-Qiu 1
通讯作者	Liu, Peitao(ptliu@imr.ac.cn)
	2023-07-24
发表期刊	EUROPEAN PHYSICAL JOURNAL PLUS
ISSN	2190-5444
卷号	138 期号:7 页码:19
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.
资助者	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
DOI	10.1140/epjp/s13360-023-04273-x
收录类别	SCI
语种	英语
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
WOS类目	Physics, Multidisciplinary
WOS记录号	WOS:001039389600004
出版者	SPRINGER HEIDELBERG
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.imr.ac.cn/handle/321006/179009
专题	中国科学院金属研究所
通讯作者	Liu, Peitao
作者单位	1.Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China 2.Taiyuan Univ Sci & Technol, Sch Mat Sci & Engn, Taiyuan 030024, Peoples R China 3.Univ Sci & Technol China, Sch Mat Sci & Engn, Shenyang 110016, Peoples R China 4.Northwestern Polytech Univ, Int Ctr Mat Discovery, Sch Mat Sci & Engn, State Key Lab Solidificat Proc, Xian 710072, 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.