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Download filepolyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics
journal contribution
posted on 2021-08-31, 13:40 authored by Rishi Gurnani, Deepak Kamal, Huan Tran, Harikrishna Sahu, Kenny Scharm, Usman Ashraf, Rampi RamprasadPolymers,
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