Machine-Learning-Assisted Synthesis of Novel MOFs

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  • Kitamura Yu
    Department of Chemistry, School of Science, Kwansei Gakuin University
  • Wakiya Takuma
    Department of Chemistry, School of Science, Kwansei Gakuin University
  • Tanaka Daisuke
    Department of Chemistry, School of Science, Kwansei Gakuin University

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Other Title
  • 機械学習を用いた新規MOFの合成条件最適化
  • キカイ ガクシュウ オ モチイタ シンキ MOF ノ ゴウセイ ジョウケン サイテキカ

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Abstract

Metal–Organic Frameworks （MOFs） exhibit promising functionalities by utilizing the framework structures. Because MOFs can form many crystal polymorphisms, it is difficult to predict synthesis condition to realize desired structures. Mechanism of crystallization process of MOFs is not fully understood, and time consuming exploration has been required to optimize the synthesis conditions. Here, we focused on machine learning techniques, i.e. cluster analysis and decision tree analysis, to improve the accuracy of the prediction for the synthesis conditions. In this work, we explored the synthesis conditions of MOFs with polynuclear metal nodes using high throughput screening systems and machine learning technique.

Journal

  • ゼオライト

    ゼオライト 39 (4), 135-143, 2022-10-15

    Japan Association of Zeolite

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