Accelerating Sustainable Epoxy Resin Development Through Bayesian Optimization and Inverse Design

Abstract

Bio‐based epoxy resins from resources like cardanol are promising sustainable alternatives, yet they typically exhibit weaker thermomechanical properties than conventional, petroleum‐based ones. To balance performance with sustainability, a machine learning (ML) framework for optimizing partially bio‐based formulations in a 13‐component design space is presented. Complementary ML strategies were empolyed: Bayesian optimization (BO) for glass transition temperature () maximization, active learning and random design for diverse dataset generation, and inverse design (ID) for the virtual screening of targeted properties. The Pareto front was refined through strategic manual experiments and virtual screening. The results demonstrate that BO effectively navigates high‐dimensional formulation spaces to find high formulations (160°C) after only 22 new experiments. The final ML model achieved a mean absolute error of ca 11°C and an coefficient of 0.91 for the prediction of , after iterative model refinement. This work establishes a data‐efficient approach for accelerating the development of sustainable bio‐based materials with tailored properties.