polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics
journal contributionposted on 2021-08-31, 13:40 authored by Rishi Gurnani, Deepak Kamal, Huan Tran, Harikrishna Sahu, Kenny Scharm, Usman Ashraf, Rampi Ramprasad
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.
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electron injection barrieryielding large librariessubtle chemical differencespolyg2g finds thousandsnovel artificial intelligencechemically valid materialsgraph translation solvespolymer dielectrics polymerslarge fractiongraph translationchemical stabilityworld applicationwide rangevalue targetstransition temperaturetechnology driverpowerhouse classpolymers designedlearned differencesknown polymersinverse problemincluding dielectricshypothetical polymersfavorable tradecost processingband gap