Research on Design of Molecules, Materials, and Processes Using Machine Learning

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Other Title
  • 機械学習を活用した分子・材料・プロセスの設計の研究例
  • キカイ ガクシュウ オ カツヨウ シタ ブンシ ・ ザイリョウ ・ プロセス ノ セッケイ ノ ケンキュウレイ

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Abstract

In researching, developing, and manufacturing highly functional materials such as membrane materials, it becomes common to utilize chemical data and chemical engineering data for machine learning to improve the efficiency of molecular design, material design, process design, and process control. It is important to construct mathematical model y ＝f (x) with high predictive ability between explanatory variables x and objective variables y, and then, y values can be predicted from x values using the constructed model, and x values can be designed to meet target y values. In this article, as examples of research in chemoinformatics, materials informatics, and process informatics, the estimation of prediction errors in new samples, modeling of metal–organic frameworks with machine learning, adaptive design of experiments with direct inverse analysis for designs of molecules, materials, and processes, and prediction of future transmembrane pressure in a drinking water treatment process are introduced.

Journal

  • MEMBRANE

    MEMBRANE 46 (6), 338-344, 2021

    THE MEMBRANE SOCIETY OF JAPAN

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