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Prediction of physical properties of thermosetting resin by using machine learning and structural formulas of raw materials

Published online by Cambridge University Press:  04 June 2020

Kokin Nakajin
Affiliation:
Research Association of High-Throughput Design and Development for Advanced Functional Materials, Tsukuba, Ibaraki305-8568, Japan Showa Denko K.K., Minato-ku, Tokyo105-0012, Japan.
Takuya Minami
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-0012, 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, Tokyo105-0012, Japan.
Katsumi Murofushi
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-0012, Japan.
Hiroshi Uchida
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-0012, Japan.
Kazuhiro Omori
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-0012, Japan.
Yoshishige Okuno
Affiliation:
Showa Denko K.K., Minato-ku, Tokyo105-0012, Japan.
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Abstract

Thermosetting resins are one of the most widely used functional materials in industrial applications. Although some of the physical properties of thermosetting resins are controlled by changing the functional groups of the raw materials or adjusting their mixing ratios, it was conventionally challenging to construct machine learning (ML) models, which include both mixing ratio and chemical information such as functional groups. To overcome this problem, we propose a machine learning approach based on extended circular fingerprint (ECFP) in this study. First, we predicted the classification of raw materials by the random forest, where ECFP was used as the explanatory variable. Then, we aggregated ECFP for each classification predicted by the random forest. After that, we constructed the prediction model by using the aggregated ECFP, feature quantities of reaction intermediates, and curing conditions of resin as explanatory variables. As a result, the model was able to predict in high accuracy (R^2 = 0.8), for example, the elastic modulus of thermosetting resins. Furthermore, we also show the result of verification of prediction accuracy in first step, such as using the one-hot-encording. Therefore, we confirmed that the properties of thermosetting resins could be predicted using mixed raw materials by the proposed method.

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Articles
Copyright
Copyright © Materials Research Society 2020

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