Specifying primary factors in the catalytic activity of metal clusters based on quantum chemical calculations and machine learning

DOI
  • Kobayashi Masato
    Department of Chemistry, Faculty of Science, Hokkaido University PRESTO, JST ESICB, Kyoto University
  • Iwasa Takeshi
    Department of Chemistry, Faculty of Science, Hokkaido University ESICB, Kyoto University
  • Gao Min
    Department of Chemistry, Faculty of Science, Hokkaido University ESICB, Kyoto University
  • Takagi Makito
    Graduate School of Chemical Sciences and Engineering, Hokkaido University
  • Maeda Satoshi
    Department of Chemistry, Faculty of Science, Hokkaido University ESICB, Kyoto University CREST, JST
  • Taketsugu Tetsuya
    Department of Chemistry, Faculty of Science, Hokkaido University ESICB, Kyoto University

Bibliographic Information

Other Title
  • 量子化学計算と機械学習を用いた金属クラスター触媒の活性因子の検討

Abstract

Because catalytic activities of metal nano clusters depend on the composition, size, environment, and structural isomers of the cluster, it has been difficult to elucidate the primary factors in their catalytic activities. In this study, we attempted to extract the factors in the catalytic activity using the sparse modeling techniques and the systematic quantum chemical calculations assisted by the automatic search of reaction pathways. In particular, the transition state energies for NO dissociation on Cu13 clusters were modeled with the orbital energies and local indices by the LASSO, SCAD, and MC+ regressions. It was found that the transition state energy negatively correlates with the LUMO energy. The SCAD and MC+ regressions could generate more compact and better models with higher correlation factors than the LASSO regression.

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Keywords

Details 詳細情報について

  • CRID
    1390001205736390784
  • NII Article ID
    130005418893
  • DOI
    10.11545/ciqs.2016.0_o18
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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