Composition optimization of a high-performance epoxy resin based on molecular dynamics and machine learning

Epoxy resin is a general term for a class of thermosetting polymers containing two or more epoxy groups in the molecule and has an excellent comprehensive performance. The properties of the resin system vary greatly due to the different compositions of the base resin, curing agent, and toughening agent. In this study, an optimization method for the multi-component epoxy resin system was put forward by using molecular dynamics simulations and machine learning methods. An optimized high- performance epoxy resin system considered Young's modulus (E), Ultimate Tensile Strength (UTS), Elongation (δ), and the glass transition temperature (Tg) together was designed by using the proposed method. The influence of each component proportion on mechanical properties can also be obtained automatically. It was found that 4,4′-Diaminodiphenyl Sulfone (DDS) was a better curing agent to improve Tg, E, and δ, compared with Dicyandiamide (DICY). Tetraglycidyl Diamino Diphenylmethane (TGDDM) could ensure high Tg, E and UTS, but the system still needed some Diglycidyl Ether of Bisphenol A (DGEBA) to improve toughness. The toughening agent Polyether Sulfone (PES) improved the toughness of the epoxy resin system significantly. The presented method could be extended to other resin system composition optimization. [Display omitted] •A resin system design method based on molecular dynamics simulation and machine learning method was developed.•The effect of different components on the material properties can also be obtained automatically, quickly and accurately by the method.•The toughening agents Polyether Sulfone can significantly improve the toughness at the same time it can still improve Young’s Modules.•The glass transition temperature improved 17.3%, the Young's Modules improved 15%, the ultimate tensile strength improved 32.7% and the specific elongation improved 85.5%.