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Prediction of repeat unit of optimal polymer by Bayesian optimization

Published online by Cambridge University Press:  24 January 2019

Takuya Minami ,
Masaaki Kawata ,
Toshio Fujita ,
Katsumi Murofushi ,
Hiroshi Uchida ,
Kazuhiro Omori  and
Yoshishige Okuno
Takuya Minami*
Affiliation:
Research Association of High-Throughput Design and Development for Advanced Functional Materials, Tsukuba, Ibaraki305-8568, Japan Showa Denko K.K., Minato-ku, Tokyo105-8518, Japan.
Masaaki Kawata
Affiliation:
National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki305-8568, Japan
Toshio Fujita
Affiliation:
Research Association of High-Throughput Design and Development for Advanced Functional Materials, Tsukuba, Ibaraki305-8568, Japan Showa Denko K.K., Minato-ku, Tokyo105-8518, Japan.
Katsumi Murofushi
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-8518, Japan.
Hiroshi Uchida
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-8518, Japan.
Kazuhiro Omori
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-8518, Japan.
Yoshishige Okuno
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-8518, Japan.
*
*(Email: [email protected])
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Abstract

Design processes of functional polymers were accelerated by adopting the Bayesian optimization; the number of trials in the process was substantially reduced. The optimization process was more than forty time accelerated to find out the target polymer compared to the random selection. The optimization efficiency was found to be successfully improved by utilizing the standard deviation of predicted probability distribution of objective function. The performance of the method was robust for dataset size in the analysis; the target polymer could be found even for a small training dataset. The proposed method is a promising tool for the high-performance polymer design, and a wide range of its applications will be expected in the polymer industry.

Keywords

polymerinformatics
Type
Articles
Information
MRS Advances , Volume 4 , Issue 19: Broader Impact/Machine Learning and Robotics , 2019 , pp. 1125 - 1130
Copyright
Copyright © Materials Research Society 2019 

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