| An Artificial Intelligence Constitutive Model for Amorphous Solids Utilizing Graph Neural Networks | |
Tao JL(陶佳乐)1,2 ; Wang YJ(王云江)1,2
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| Corresponding Author | Wang, Yun-Jiang([email protected]) |
| Source Publication | JOM
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| 2024-07-12 | |
| Pages | 8 |
| ISSN | 1047-4838 |
| Abstract | Constructing an efficient constitutive model for the deformation of amorphous solids has long been a challenging yet important objective in materials science. The difficulty lies in the structure-less characteristics of amorphous materials, in which it is not an easy task to extract physically meaningful knowledge-based descriptors for constitutive equations. In contrast to traditional constitutive modeling, machine learning (ML)-based models do not rely on intricate thermodynamics and kinetics of materials, emerging as an alternative. Here, we propose a graph-based constitutive model employing the cutting-edge graph neural network (GNN) techniques to investigate the deformation behavior of amorphous solids, with Cu50Zr50 metallic glass (MG) as a prototypical amorphous material to test the idea. By integrating atomic strain information with graph topology, the GNN model successfully reproduces stress-strain responses of MGs across all tested temperatures and strain rates and exhibits good transferability, showcasing the potential of GNNs in establishing a universal constitutive law for amorphous solids from a data-driven perspective. |
| DOI | 10.1007/s11837-024-06742-9 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001270064400004 |
| WOS Keyword | METALLIC GLASSES ; DEFORMATION ; DYNAMICS ; BEHAVIOR ; FRACTURE ; FLOW |
| WOS Research Area | Materials Science ; Metallurgy & Metallurgical Engineering ; Mineralogy ; Mining & Mineral Processing |
| WOS Subject | Materials Science, Multidisciplinary ; Metallurgy & Metallurgical Engineering ; Mineralogy ; Mining & Mineral Processing |
| Funding Project | Strategic Priority Research Program of Chinese Academy of Sciences[XDB0620103] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB0510301] ; Strategic Priority Research Program ; Youth Innovation Promotion Association of Chinese Academy of Sciences[12072344] ; National Natural Science Foundation of China |
| Funding Organization | Strategic Priority Research Program of Chinese Academy of Sciences ; Strategic Priority Research Program ; Youth Innovation Promotion Association of Chinese Academy of Sciences ; National Natural Science Foundation of China |
| Classification | 二类 |
| Ranking | 1 |
| Contributor | Wang, Yun-Jiang |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/96048 |
| Collection | 非线性力学国家重点实验室 |
| Affiliation | 1.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; 2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China |
| Recommended Citation GB/T 7714 | Tao JL,Wang YJ. An Artificial Intelligence Constitutive Model for Amorphous Solids Utilizing Graph Neural Networks[J]. JOM,2024:8.Rp_Au:Wang, Yun-Jiang |
| APA | 陶佳乐,&王云江.(2024).An Artificial Intelligence Constitutive Model for Amorphous Solids Utilizing Graph Neural Networks.JOM,8. |
| MLA | 陶佳乐,et al."An Artificial Intelligence Constitutive Model for Amorphous Solids Utilizing Graph Neural Networks".JOM (2024):8. |
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