An Artificial Intelligence Constitutive Model for Amorphous Solids Utilizing Graph Neural Networks
文献类型：期刊论文
| 作者 | Tao JL(陶佳乐)1,2 ; Wang YJ(王云江)1,2
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| 刊名 | JOM
 |
| 出版日期 | 2024-07-12 |
| 页码 | 8 |
| ISSN号 | 1047-4838 |
| DOI | 10.1007/s11837-024-06742-9 |
| 通讯作者 | Wang, Yun-Jiang(yjwang@imech.ac.cn) |
| 英文摘要 | 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. |
| 分类号 | 二类 |
| WOS关键词 | METALLIC GLASSES ; DEFORMATION ; DYNAMICS ; BEHAVIOR ; FRACTURE ; FLOW |
| 资助项目 | 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 |
| WOS研究方向 | Materials Science ; Metallurgy & Metallurgical Engineering ; Mineralogy ; Mining & Mineral Processing |
| 语种 | 英语 |
| WOS记录号 | WOS:001270064400004 |
| 资助机构 | 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 |
| 其他责任者 | Wang, Yun-Jiang |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/96047]  |
| 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 100049, Peoples R China 2.Chinese Acad Sci, Inst Mech, State Key Lab Nonlinear Mech, Beijing 100190, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Tao JL,Wang YJ. An Artificial Intelligence Constitutive Model for Amorphous Solids Utilizing Graph Neural Networks[J]. JOM,2024:8. |
| 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. |
入库方式： OAI收割
来源：力学研究所
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