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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

Machine Learning Framework for Predicting High-Strain-Rate Response Under Extreme Loading

Author(s)

Tyler A. Collins, Sara Adibi

On-Site Speaker (Planned)

Tyler A. Collins

Abstract Scope

High-strain-rate material response under extreme loading is governed by complex, multiscale mechanisms that are challenging to capture using conventional simulation approaches alone. In this work, we present a machine learning framework for predicting mechanical and thermodynamic response under dynamic loading conditions using simulation-informed data. The framework leverages physically motivated descriptors to learn structure–response relationships, enabling rapid prediction of material behavior at high strain rates. The approach is demonstrated through a representative material system subjected to extreme loading and validated against experimental trends reported in the literature. By reducing reliance on large-scale, high-fidelity simulations, the proposed methodology significantly lowers computational cost while preserving physical consistency. More broadly, this framework is designed to be transferable across material classes, providing a scalable pathway for modeling high-strain-rate behavior and accelerating materials design for extreme environments.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Polymers, Modeling and Simulation

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