Designing formulations of bio-based, multicomponent epoxy resin systems via machine learning

Petroleum-based epoxy resins are commonly used as a matrix in fiber-reinforced polymer composites. Bio-based epoxy resin systems could be a more environmentally friendly alternative to conventional epoxy resins. In this work, novel formulations of multicomponent, amino acid-based resin systems exhibiting high or low glass-transition temperatures ( T_g ) were designed via Bayesian optimization and active learning techniques. After only five high- T_g experiments, thermosets with T_g already higher than those of the individual components were obtained, pointing out the existence of synergistic effects among the amino acids used and confirming the efficiency of the theoretical design. Linear and nonlinear machine learning (ML) models successfully predicted T_g with a mean absolute error of 3.98 ^∘C and R^2 score of 0.91. A price reduction of up to 13.7