Polymer design using genetic algorithm and machine learning

Abstract

Data driven or machine learning (ML) based methods have been recently used in materials science to provide quick material property predictions. Although powerful and robust, these predictive models are still limited in terms of their applicability towards the design of materials with target property or performance objectives. Here, we employ a nature-mimicking optimization method, the genetic algorithm, in tandem with ML-based predictive models to design polymers that meet practically useful, but extreme, property criteria (i.e., glass transition temperature, Tg>500 K and bandgap, Eg>6 eV). Analogous to nature, the characteristic properties of a polymer are assumed to be determined by the constituting types and sequence of chemical building blocks (or fragments) in the monomer unit. Evolution of polymers by natural operations of crossover, mutation, and selection over 100 generations leads to creation of 132 new (as compared to 4 already known cases) and chemically unique polymers with high Tg and Eg. Chemical guidelines on what fragments make up polymers with extreme thermal and electrical performance metrics have been selected and revealed by the algorithm. The approach presented here is general and can be extended to design polymers with different property objectives.