Characterization of Multi-scale Structure-Property Relationships for Thermosetting Resins by Machine Learning and Molecular Dynamics Methods

Abstract

We conducted machine learning studies of epoxy resin for Structure-property relationship analyses of mechanical and thermal properties. The training sets were generated by full atomistic molecular dynamics calculations. Since the physical properties of thermosetting resins are strongly dependent on the higher-order structure formed by crosslinking reactions, it is necessary to develop a property prediction model that takes into account not only the molecular structure of the pre-polymer but also the higher-order structure of the resin after curing. In this study, in addition to the regression analysis with molecular descriptors and vectorized molecular fingerprints of pre-polymers, higher-order structure–property relationship analysis and molecular descriptor–higher-order structure correlation analysis were carried out using topological data analysis (TDA) techniques such as persistent homology. By connecting multi-scale structures with learning models, we were able to achieve both prediction accuracy and understanding of the phenomena which are necessary for inverse analysis to find new materials.