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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 Hyperparameter-Informed Sparse Bayesian Active Learning for Understanding and Controlling Frontal Radical Polymerization
Author(s) Philip P Lampkin, Darya A Ivannikava, Andrew R. Falkowski, Matthew S. Sigman, Taylor D. Sparks, Jacob J. Lessard
On-Site Speaker (Planned) Taylor D. Sparks
Abstract Scope Conventional free radical polymerization requires prolonged heating and added solvent, raising the energy and resource costs of plastic manufacturing. Frontal radical polymerization (FRaP) is solvent-free: a brief thermal or photonic stimulus initiates a self-propagating reaction cascade sustained by the heat the polymerization releases. Adoption is limited by reaction instability, poor control over material properties, and little understanding of which chemical features govern reaction outcome. We developed a Bayesian active learning workflow that combines a sparse axis-aligned subspace prior and hyperparameter-informed predictive exploration with hundreds of high-fidelity molecular descriptors, building interpretable surrogate models that identify the features controlling reactions and predict outcomes. We used the workflow to guide an experimental campaign on FRaP reactions with binary and ternary mixtures of commercial initiators and the plastics they produce. The models identify useful formulations and map the multidimensional property space accessible through FRaP, enabling rational plastic design and lowering the barrier to adoption.
Proceedings Inclusion? Planned:
Keywords Polymers, Computational Materials Science & Engineering, Process Technology

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