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Discovering high-strength alloys via physics-transfer learning

文献类型：期刊论文

作者Zhao, Yingjie2; Zhou HB(周红波)3; Zhang, Zian2; Bo, Zhenxing4; Sun, Baoan4; Jiang MQ(蒋敏强)1,3; Xu, Zhiping2
刊名MATTER
出版日期2025-09-03
卷号8期号:9页码:15
ISSN号2590-2393
DOI10.1016/j.matt.2025.102377
通讯作者Jiang, Minqiang(mqjiang@imech.ac.cn) ; Xu, Zhiping(xuzp@tsinghua.edu.cn)
英文摘要Predicting the strength of materials requires knowledge over multiple scales, striking a balance between accuracy and efficiency. Peierls stress measures material strength by evaluating dislocation resistance to plastic flow, reliant on elastic lattice responses and the crystal slip energy landscape. Computational challenges due to the non-local and non-equilibrium nature of dislocations prohibit Peierls stress evaluation from stateof-the-art material databases. We propose a physics-transfer learning framework that leverages neural networks trained on force-field simulations to understand crystal plasticity physics, predicting Peierls stress from material parameters derived via first-principles calculations, which are otherwise computationally intractable for direct dislocation modeling. This physics-transfer approach successfully screens strengths of metallic alloys from a limited number of single-point calculations with chemical accuracy. Guided by the prediction, we fabricate high-strength binary alloys previously unexplored via high-throughput ion-beam deposition. The framework solves problems facing the accuracy-performance dilemma by harnessing multi-scale physics in materials sciences.
分类号一类
WOS关键词MECHANICAL-PROPERTIES ; DISLOCATIONS ; PLASTICITY ; STRESS
资助项目National Natural Science Foundation of China[12425201] ; National Natural Science Foundation of China[52090032] ; National Natural Science Foundation of China[12125206] ; National Natural Science Foundation of China[W2411003] ; Beijing Municipal Science and Technology Commission[Z23110000 7123015]
WOS研究方向Materials Science
语种英语
WOS记录号WOS:001566113700001
资助机构National Natural Science Foundation of China ; Beijing Municipal Science and Technology Commission
其他责任者蒋敏强 ; Xu, Zhiping
源URL[http://dspace.imech.ac.cn/handle/311007/103593]  
专题力学研究所_非线性力学国家重点实验室
作者单位1.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 101408, Peoples R China
2.Tsinghua Univ, Dept Engn Mech, Appl Mech Lab, Beijing 100084, Peoples R China;
3.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China;
4.Chinese Acad Sci, Inst Phys, Beijing 100190, Peoples R China;
推荐引用方式
GB/T 7714		 Zhao, Yingjie,Zhou HB,Zhang, Zian,et al. Discovering high-strength alloys via physics-transfer learning[J]. MATTER,2025,8(9):15.
APA Zhao, Yingjie.,周红波.,Zhang, Zian.,Bo, Zhenxing.,Sun, Baoan.,...&Xu, Zhiping.(2025).Discovering high-strength alloys via physics-transfer learning.MATTER,8(9),15.
MLA Zhao, Yingjie,et al."Discovering high-strength alloys via physics-transfer learning".MATTER 8.9(2025):15.

入库方式： OAI收割

来源：力学研究所

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