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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

Data-Driven Prediction of High-Strain-Rate Behavior in Polyurea from Molecular Dynamics Simulations

Author(s)

Tyler A. Collins, Sara Adibi

On-Site Speaker (Planned)

Tyler A. Collins

Abstract Scope

Polyurea is widely employed in construction and defense industries due to its exceptional blast and impact mitigation properties, derived from a distinct microstructure featuring semi-crystalline hard segments and flexible soft segments. To efficiently investigate the mechanisms underlying material failure, we propose employing machine learning to predict the thermodynamic response of polyurea subjected to high-strain-rate conditions. The model will be trained on data generated by molecular dynamics simulations and validated against experimental benchmarks from existing literature. This predictive framework aims to significantly reduce computational demands compared to traditional all-atom simulation approaches. Ultimately, our methodology is expected to enhance understanding of polyurea's complex failure mechanisms, facilitating accelerated materials design and optimization for protective applications.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Polymers, Modeling and Simulation

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