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

Wu, Stephen; Kondo, Yukiko; Kakimoto, Masa‐aki; Yang, Bin; Yamada, H.; Kuwajima, Isao; Lambard, G.; Hongo, Kenta; Xu, Yibin; Shiomi, Junichiro; Schick, Christoph; Morikawa, Junko; Yoshida, Ryo

doi:10.1038/s41524-019-0203-2

articlenpj Computational MaterialsJun 21, 2019GOLD OA

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

SWStephen Wu YKYukiko Kondo MKMasa‐aki Kakimoto BYBin Yang HYH. Yamada

The Graduate University for Advanced Studies, SOKENDAI · The Institute of Statistical Mathematics · +8 more institutions

Indexed incrossrefdoaj

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…

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493

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References: 51

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Topics & keywords

Topics

Keywords

Polymer
Monomer
Thermal conductivity
Materials science
Polymerization
Computer science
Nanotechnology
Algorithm

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