Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

Abstract

Abstract The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials. However, its practical benefits still remain unproven in real-world applications, particularly in polymer science. We demonstrate the successful discovery of new polymers with high thermal conductivity, inspired by machine-learning-assisted polymer chemistry. This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data, expertise from laboratory synthesis and advanced technologies for thermophysical property measurements. Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties, we identified thousands of promising hypothetical polymers. From these candidates, three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications. The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK, which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.

Abstract of "Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm" by Stephen Wu, Yukiko Kondo, Masa‐aki Kakimoto et al., https://doi.org/10.1038/s41524-019-0203-2, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.