Issue 2, 2021

Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining

Abstract

Direct design of low temperature oxidative coupling of methane (OCM) catalysts is proposed via machine learning and data mining. 58 OCM catalysts are experimentally synthesized and evaluated. The collected 58 sets of data are then classified by unsupervised machine learning in a multi-dimensional space where an active catalyst group for low temperature OCM is identified. Data mining then identifies the physical rule within the group. Catalysts satisfying such a physical rule are designed where 2 undiscovered low temperature OCM catalysts are found and experimentally validated. Thus, machine learning and data mining reveal the hidden physical rule behind the catalysis leading to the direct design of catalysts. Hence, machine learning and data mining open up the insight on a powerful strategy for designing catalysts.

Graphical abstract: Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining

Supplementary files

Article information

Article type
Paper
Submitted
07 Sep 2020
Accepted
29 Oct 2020
First published
30 Oct 2020

Catal. Sci. Technol., 2021,11, 524-530

Author version available

Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining

J. Ohyama, T. Kinoshita, E. Funada, H. Yoshida, M. Machida, S. Nishimura, T. Uno, J. Fujima, I. Miyazato, L. Takahashi and K. Takahashi, Catal. Sci. Technol., 2021, 11, 524 DOI: 10.1039/D0CY01751E

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