Strategies for the fast optimization of the glass transition temperature of sustainable epoxy resin systems via machine learning

Abstract

AbstractAligned with the prevailing sustainability paradigm, the imperative adoption of bio‐based substitutes for constituents within petroleum‐derived epoxy resin becomes evident. Blending bio‐based and petroleum‐based epoxy resins and curing agents, establishes a synergistic compromise addressing both sustainability imperatives and the mechanical efficacy of thermosets. The conventional approach to discovering optimal compositions for multi‐component mixtures under specific boundary conditions includes empirical trial and error and is seen as a protracted and inefficient endeavor. Conversely, leveraging machine learning might afford a streamlined and confident resolution to this challenge. This investigation elucidates the requisite strategies for maximizing the efficiency of material property optimization through the application of Bayesian optimization and active learning. Illustratively, the study demonstrates the proficient optimization of the glass transition temperature within a four‐component epoxy resin system. This optimization is conducted across varying ranges of bio‐content and cost considerations. The study underscores the utility of machine learning in achieving this task with notable efficiency. The efficacy of least squares, kernel ridge regression, Gaussian process regression, and artificial neural networks, is meticulously evaluated through comprehensive seven‐fold cross‐validation and validated against experimental data.