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Meeting

2027 TMS Annual Meeting & Exhibition

Symposium

Computational Discovery and Design of Materials

Presentation Title

Assessing, training, and fine-tuning foundation potentials for plasticity-relevant properties of metals

Author(s)

Shuozhi Xu, Zhihao Huang, Subah Mubassira, Anish Dhungana, Xiang-Guo Li

On-Site Speaker (Planned)

Shuozhi Xu

Abstract Scope

Foundation potentials are machine learning models trained on large, diverse datasets to provide transferable atomistic simulations across a wide range of chemical elements, offering a promising foundation for accelerating computational materials discovery. Despite their broad elemental coverage, these models are not specifically tailored to any particular material system, which can limit their predictive accuracy for targeted applications. Before employing foundation potentials with confidence, it is therefore essential to assess their performance for the materials and properties of interest, which are plasticity-relevant properties of metals in this work. Following such assessment, we improve the accuracy of foundation potentials through two strategies: retraining a foundation potential from scratch and fine-tuning an existing foundation potential, respectively. We further identify the key factors — including descriptor, transform, and model architecture — that most strongly influence the efficiency and accuracy of foundation potentials.

Proceedings Inclusion?

Planned:

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