Discovery of Copolymer Resins with Optimal Viscosity–Toughness–Heat Resistance Trade-Offs via the Material Genome Approach

Abstract

Accelerated discovery of polymer materials for specific applications requires the achievement of multiple, sometimes competing design objectives. Vast chemical space and the absence of well-established structure–processing–property relationships have considerably challenged the accelerated discovery of polymer materials. Herein, taking high-performance copolymer resins as an example, we propose a generalizable pipeline that integrates the screening of molecular structures, experimental validation of properties, and chemical interpretability to address both of these challenges. By means of the material genome paradigm, machine learning models are established for the structure–property relationship in conjunction with the additional viscosity for the evaluation of processing performance. Importantly, molecules of copolymer resins with low viscosity, high toughness, and high heat resistance are designed for a large search space. Experimental studies further verify that the newly designed copolymer resins possess excellent comprehensive performance, which cannot be achieved through time-consuming trial-and-error design methods. In addition, gene analysis is employed to interpret the structure–processing–property relationships of copolymer resins. The machine-learning-assisted material genome pipeline proposed in this work provides a promising strategy for the accelerated design of molecular structures in a large search space and for the understanding of structure–property relationships from data-informed perspectives.