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.