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Exploring High Thermal Conductivity Amorphous Polymers Using Reinforcement Learning
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DOI：10.1021/acsami.1c23610.png)
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摘要
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Developing amorphous polymers with desirable thermal conductivity has significant implications, as they are ubiquitous in applications where thermal transport is critical. Conventional Edisonian approaches are slow and without guarantee of success in material development. In this work, using a reinforcement learning scheme, we design polymers with thermal conductivity above 0.400 W/m.K. We leverage a machine learning model trained against 469 thermal conductivity data calculated from high-throughput molecular dynamics (MD) simulations as the surrogate for thermal conductivity prediction, and we use a recurrent neural network trained with around one million virtual polymer structures as a polymer generator. For all generated polymers with thermal conductivity >= 0.400 W/m.K, we have evaluated their synthesizability by calculating the synthetic accessibility score and validated the thermal conductivity of selected polymers using MD simulations. The best thermally conductive polymer designed has an MD-calculated thermal conductivity of 0.693 W/m.K, which is also estimated to be easily synthesizable. Our demonstrated inverse design scheme based on reinforcement learning may advance polymer development with target properties, and the scheme can also he generalized to other material development tasks for different applications.
Keyword:
polymer design
reinforcement learning
thermal conductivity
molecular dynamics simulation
synthetic accessibility
期刊
IF:
8.2
论文数:
6.1W
被引数:
38.7W
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