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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title From Loading to Architecture: A Physics-Guided Machine-Learning Framework for Polymer Spall Strength
Author(s) Tyler A. Collins, Sara Adibi
On-Site Speaker (Planned) Tyler A. Collins
Abstract Scope Polymer spall simulations yield sparse, costly datasets in which loading, material identity, and stochastic fracture variability are entangled. We present a physics-guided, interpretable machine-learning framework for separating these contributions in molecular-dynamics predictions of spall strength. Analyses of polyurethane, polyurea, and polyethylene across impact velocities establish a material- and loading-aware baseline together with an empirical estimate of run-to-run variability. The framework defines baseline-conditioned residuals as targets for architecture-sensitive modeling and uses predictive uncertainty to flag extrapolation beyond the sampled domain. Ongoing simulations systematically vary polyurea hard-segment fraction; resulting residual responses will be related to hard/soft composition, block run lengths, and structural-heterogeneity metrics. This study asks whether architecture effects are resolvable above simulation variability and demonstrates a data-efficient workflow for converting small simulation datasets into testable design hypotheses for impact-resistant polymers.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Polymers

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