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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 A Physics-Informed Inverse Design Pipeline for Halogen-Free Fire-Retardant Polymers
Author(s) Ishanee Khadakkar, Brian Lattimer
On-Site Speaker (Planned) Ishanee Khadakkar
Abstract Scope We developed a physics-informed machine-learning pipeline that predicts polymer fire properties from combustion- and structure-based molecular descriptors. Predicted properties are used as inputs to Fire Dynamics Simulator (FDS) cone calorimeter models to evaluate fire response. Experimental peak heat release rate and time-to-ignition data were used to identify polymers meeting MIL-STD-2031 and NAVSEA DDS-078-1 requirements. These polymers were used to construct a fragment library for a supervised, iterative molecular generation cycle to explore the design space and generate new candidate polymers, which were subsequently evaluated by the pipeline for predicted fire performance. With the current number of design iterations, halogenated chemistries exhibited the best fire performance. However, the framework identified benzoxazole-based halogen-free polymers as non-toxic alternatives. Selected candidates will be synthesized and validated by cone calorimetry. This work establishes a physics-guided inverse-design framework integrating interpretable fire-property prediction, FDS simulation, and experimental validation to accelerate halogen-free fire-retardant polymer design for naval applications.
Proceedings Inclusion? Planned:
Keywords Polymers, Computational Materials Science & Engineering, Machine Learning

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