Journal
CHEMISTRY OF MATERIALS
Volume 33, Issue 17, Pages 7008-7016Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.chemmater.1c02061
Keywords
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Funding
- Center for Understanding and Control of Acid GasInduced Evolution of Materials for Energy (UNCAGE ME), an Energy Frontier Research Center - U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES) [DE-SC0012577]
- Office of Naval Research through a Multi-University Research Initiative (MURI) [N00014-17-1-2656]
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polyG2G is a novel AI-based approach for polymer design that uses graph-to-graph translation to address the inverse problem of polymer design, generating large libraries of high-performance hypothetical polymers. Density functional theory simulations confirm that a large fraction of polymers designed by polyG2G are indeed of high value.
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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