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arXiv · 2412.08407

Polymer Composites Informatics for Flammability, Thermal, Mechanical and Electrical Property Predictions

Huan Tran·Chiho Kim·Rishi Gurnani·Oliver Hvidsten·Justin DeSimpliciis·Rampi Ramprasad·Karim Gadelrab·Charles Tuffile·Nicola Molinari·Daniil Kitchaev·Mordechai Kornbluth
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Abstract

Polymer composite performance depends significantly on the polymer matrix, additives, processing conditions, and measurement setups. Traditional physics-based optimization methods for these parameters can be slow, labor-intensive, and costly, as they require physical manufacturing and testing. Here, we introduce a first step in extending Polymer Informatics, an AI-based approach proven effective for neat polymer design, into the realm of polymer composites. We curate a comprehensive database of commercially available polymer composites, develop a scheme for machine-readable data representation, and train machine-learning models for 15 flame-resistant, mechanical, thermal, and electrical properties, validating them on entirely unseen data. Future advancements are planned to drive the AI-assisted design of functional and sustainable polymer composites.

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Huan Tran, Chiho Kim, Rishi Gurnani, Oliver Hvidsten, Justin DeSimpliciis, Rampi Ramprasad, Karim Gadelrab, Charles Tuffile, Nicola Molinari, Daniil Kitchaev, Mordechai Kornbluth. 2024-12-11. Polymer Composites Informatics for Flammability, Thermal, Mechanical and Electrical Property Predictions. https://doi.org/10.1039/d4py01417k

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