Polymers, due to advantages such as low-cost processing, chemical stability, low density and tuneable design, have emerged as a powerhouse class of materials. However, precisely because the design space is so large, traditional approaches (be it experiment or pure simulation) for identifying application-specific polymers are often infeasible: they simply take too long. To accelerate the search, we need a radically different approach, the most promising of which are driven by artificial intelligence, AI, and therefore offer ultrafast predictions. Here, we present a novel AI platform for the generative design of polymers and use it to discover promising dielectric materials. The key insight is that the distribution of subtle chemical differences between high- and low-performing polymers can be learned and then sampled to generate hypothetical, high-performing materials. Our AI finds tens of thousands of dielectric polymers which meet extreme objectives. Density functional theory simulations of bandgap and electron injection barrier confirm that, out of a small subset, 50% of these hypothetical polymers do indeed match the objectives.

PolyG2G: A novel generative machine learning algorithm applied to polymer dielectric design

ACS Fall 2021

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