Bayesian Optimization of flame-retardant performance in a high-Tg epoxy resin system

Title data

Krebs, Niko ; Demleitner, Martin ; Albuquerque, Rodrigo Q. ; Schartel, Bernhard ; Ruckdäschel, Holger:
Bayesian Optimization of flame-retardant performance in a high-Tg epoxy resin system.
In: Computational Materials Science. Vol. 260 (2025) . - 114210.
ISSN 1879-0801
DOI: https://doi.org/10.1016/j.commatsci.2025.114210

Project information

Abstract in another language

Polymeric materials are widely used due to their mechanical properties and cost-effectiveness, but their inherent flammability requires effective flame-retardant additives to meet safety standards. Optimizing multi-component flame-retardant formulations is challenging due to the vast experimental space. This study applies Bayesian Optimization (BO) to optimize flame-retardant formulations in high glass transition temperature (Tg) epoxy resins. Aluminum diethyl phosphinate (AlPi) was systematically combined with three synergists: zinc stannate (ZnSt), a silicone-based additive (DowSil), and low-melting glass frits (Ceepree). BO-guided experimental design expanded from 16 initial formulations to a total of 28, minimizing the Maximum Average Rate of Heat Emission (MARHE) under the constraint of Total Smoke Production (TSP) < 17 m2 using the epsilon-constraint method. BO revealed non-linear synergistic interactions: ZnSt significantly reduced smoke production while AlPi effectively lowered heat release. The optimized formulation (BO7) achieved the lowest MARHE (122 kW/m2) while maintaining acceptable smoke levels, establishing a new Pareto front. The results demonstrate the effectiveness of BO in accelerating the development of synergistic, halogen-free flame-retardant polymer systems, offering a scalable and sustainable approach to polymer formulation design.