Data-driven design and green preparation of bio-based flame retardant polyamide composites

Abstract

This work introduces, for the first time, an innovative bio-based flame retardant (FR) system for biocomposites, integrating experimental insights and machine learning (ML) to optimize both composition and performance. By employing a computationally guided, cost-efficient experimentation strategy, we systematically combine design of experiments for space exploration, ML-driven property prediction, and optimization methods to rapidly identify high-performance formulations. Crucially, this approach demonstrates how data-driven techniques can be seamlessly incorporated into conventional experimental material design, ensuring proper sampling of the design space and leveraging the collected data to generate new predictions and optimize the properties of these sustainable materials. As a result, mechanical strength is significantly enhanced and fire safety improved, minimizing reliance on resource-intensive trial-and-error processes. The optimal formulation achieved an 18.4% increase in tensile strength (TS) and a 53.1% reduction in the peak heat release rate (pHRR) compared to the neat polymer. Bayesian optimization further validated individual optimal solutions, delivering up to a 22.3% improvement in TS and a 73.7% reduction in pHRR. Overall, this research establishes a digitally integrated workflow that accelerates the development of sustainable, high-performance biocomposites and bio-based flame retardants, providing eco-friendly alternatives to conventional fire-safe polymeric materials.