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

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.

Supplementary files

Article information

Article type
Paper
Submitted
11 Dec 2024
Accepted
01 Jul 2025
First published
03 Jul 2025

Polym. Chem., 2025, Accepted Manuscript

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

H. D. Tran, C. Kim, R. Gurnani, O. Hvidsten, J. DeSimpliciis, R. Ramprasad, K. Gadelrab, C. Tuffile, N. Molinari, D. Kitchaev and M. Kornbluth, Polym. Chem., 2025, Accepted Manuscript , DOI: 10.1039/D4PY01417K

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