@Article{cmc.2026.079587,
AUTHOR = {Chuhan Zhang, Jinguo You, Jialin Xu, Mingqian Li, Xiaofeng Chen, Jingmei Tao, Caiju Li, Jianhong Yi},
TITLE = {A Graph-Based Spatio-Temporal Attention Network for Stress–Strain Behavior Prediction of Copper-Based Composites},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/26558},
ISSN = {1546-2226},
ABSTRACT = {With the rapid development of artificial intelligence and data-driven modeling, deep learning has become an effective tool for analyzing scientific discovery such as predicting material behaviors. Graphene-reinforced copper-based composites, which exhibit excellent mechanical, electrical, and thermal properties, have attracted extensive attention in advanced engineering applications; however, accurate prediction of their stress–strain behavior still relies heavily on computationally expensive molecular dynamics simulations or experiments. In this work, we propose a Graph-based Spatio-Temporal Attention Network, termed GraphSTAN, for stress–strain behavior prediction of copper-based composites. Specifically, atomic-scale initial microstructures are encoded as graphs and integrated with static physical parameters. A topology-aware spatio-temporal feature interaction mechanism is introduced to effectively couple structural representations with stress–strain time-series dynamics, enabling accurate prediction of full stress–strain evolution. Moreover, a multi-features dataset is constructed based on LAMMPS molecular dynamics simulations, consisting of 596 independent simulation samples corresponding to distinct combinations of microstructural configurations, loading conditions, and stress–strain time series. Experimental results demonstrate that GraphSTAN effectively predicts full stress–strain curves and achieves the higher performance of <mml:math id="mml-ieqn-1"><mml:msup><mml:mrow><mml:mi mathvariant="normal">R</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:math>, MAE and RMSE for yield strength and Young’s modulus, respectively, significantly outperforming baseline methods.},
DOI = {10.32604/cmc.2026.079587}
}