Work

Accelerating Sustainable Epoxy Resin Development Through Bayesian Optimization and Inverse Design

Year: 2026

Type: article

Abstract: 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.

Source: Journal of Applied Polymer Science

DOI: 10.1002/app.71143

Authors: Natalie Wunder, Rodrigo Q. Albuquerque, Florian Max, Holger Ruckdäschel

Institutions: University of Bayreuth, Neue Materialien Bayreuth GmbH

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: hybrid

Estimated APC paid: $4,140