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

Abstract

Abstract 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 ($$T_{{\text{g}}}$$ T g ) were designed via Bayesian optimization and active learning techniques. After only five high-$$T_{{\text{g}}}$$ T g experiments, thermosets with $$T_{{\text{g}}}$$ 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 $$T_{{\text{g}}}$$ T g with a mean absolute error of 3.98$$^{\circ }{\text{C}}$$ ∘ C and $$R^2$$ R 2 score of 0.91. A price reduction of up to 13.7% was achieved while maintaining the $$T_{{\text{g}}}$$ T g of 130$$^{\circ }{\text{C}}$$ ∘ C using an optimized formulation. The LASSO model provided information about the dependence of $$T_{{\text{g}}}$$ T g on the number of active hydrogen atoms and aromaticity. This study highlights the importance of Bayesian optimization and ML to achieve a more sustainable development of epoxy resin materials. Impact statement This article shows how the sustainability of epoxy resin systems (ERSs) can be significantly improved by combining experimental and theoretical strategies. First, amino acids are used as curing agents in multicomponent formulations to produce bio-based ERSs. Second, the number of trial-and-error experiments required to obtain formulations with high or low glass-transition temperatures (Tg) is greatly reduced using machine learning (ML) strategies to design all experiments. Not only is it shown how Tg can be maximized in only five new theoretically designed formulations, but the economic advantages of the proposed approach are also discussed. The trends between Tg and the type of optimized biocomponents are discussed based on the unambiguous interpretation of the best-trained ML model. The results presented in this study pave the way for the theoretical design of more sustainable polymeric materials. Graphical abstract