polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics

Abstract

Polymers, due to advantages such as low-cost processing, chemical stability, low density, and tunable design, have emerged as a powerhouse class of materials for a wide range of applications, including dielectrics. However, in certain applications, the performance of dielectrics is limited by insufficient electric breakdown strength. Using this real-world application as a technology driver, we describe a novel artificial intelligence (AI)-based approach for the design of polymers. We call this approach polyG2G. The key concept underlying polyG2G is graph-to-graph translation. Graph-to-graph translation solves the inverse problem. First, the subtle chemical differences between high- and low-performing polymers are learned. Then, the learned differences are applied to known polymers, yielding large libraries of novel, high-performing, hypothetical polymers. Our approach, with respect to a host of presently adopted design methods, exhibits a favorable trade-off between generation of chemically valid materials and available chemical search space. polyG2G finds thousands of potentially high-value targets (in terms of glass-transition temperature, band gap, and electron injection barrier) from an otherwise intractable search space. Density functional theory simulations of band gap and electron injection barrier confirm that a large fraction of the polymers designed by polyG2G are indeed of high value. Finally, we find that polyG2G is able to learn established structure–property relationships.