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

TL;DR

This paper extends Polymer Informatics with machine learning models trained on a curated database to predict multiple properties of polymer composites, aiming to accelerate design and optimization processes.

Contribution

It introduces a comprehensive database and machine-readable data scheme for polymer composites, enabling AI-based property prediction for the first time.

Findings

Validated ML models on unseen data for 15 properties

Demonstrated potential for AI-driven composite design

Established groundwork for sustainable polymer development

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.