Machine-learning-driven discovery of polymers molecular structures with high thermal conductivity

Abstract The ability to efficiently design new and advanced polymers with functional thermal properties is hampered by the high-cost and time-consuming experiments. Machine learning is an effective approach that can accelerate materials development by combining material science and big data techniques. Here, machine learning methods were used to predict the thermal conductivity of various single-chain polymers, and the relationship between molecular structures of polymer repeating units and thermal conductivity was also been investigated. The predict model starts from a benchmark dataset generated by large-scale molecular dynamics computations. In predict models, the polymers were ‘fingerprinted’ as simple, easily attainable numerical representations, which helps to develop an on-demand property prediction model. Further, potential quantitative relationship between molecular structures of polymer and thermal conductivity property was analyzed, and hypothetical polymers with ideal thermal conductivity were identified. The methods are shown to be general, and can hence guide the screening and systematic identification of high thermal conductivity.