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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Effects of void size and material properties on hydrostatic compression of a single crystal: A combined atomistic simulation and deep learning study

Author(s)

Mahshad Fani, William Chadwell, Nishad Tasnim, Xin Wang, Mohammad Younes Araghi, Kun Lu, Zejian Zhou, Tang Gu, Shuozhi Xu

On-Site Speaker (Planned)

Mahshad Fani

Abstract Scope

The growth and collapse of voids play an important role in the plastic deformation of ductile crystals. In this work, atomistic simulations are conducted to analyze the effects of void size and material properties, including intrinsic and unstable stacking fault energies (ISFE/USFE) and surface energy (SE), on the hydrostatic compression of a Cu single crystal. A total of eleven interatomic potentials that differ in ISFE, USFE, and SE but keep all other material properties almost the same are applied. It is found that the critical pressure for complete void closure increases as the ISFE increases or the USFE decreases, while there exists an intermediate SE that leads to a minimum critical pressure. Further quantitative analysis using two deep learning models reveals that the SE is the most important factor in determining the critical pressure. Our work highlights the impact that can be made by combining physics-based and data-driven approaches.

Proceedings Inclusion?

Planned:

Keywords

Modeling and Simulation, Machine Learning, Computational Materials Science & Engineering

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