Direct design of active catalysts for low temperature oxidative coupling of methane via machine learning and data mining. Issue 2 (18th November 2020)

• Record Type: Journal Article
• Authors: Ohyama, Junya; Kinoshita, Takaaki; Funada, Eri; Yoshida, Hiroshi; Machida, Masato; Nishimura, Shun; Uno, Takeaki; Fujima, Jun; Miyazato, Itsuki; Takahashi, Lauren; Takahashi, Keisuke
• Is Part Of: Catalysis science & technology. Volume 11:Issue 2(2021)
• Page Start: 524
• Page End: 530
• Publication Date: 2020-11-18
• Subjects: Catalysis -- Periodicals; 541.395
• DOI: 10.1039/d0cy01751e
• Languages: English
• ISSNs: 2044-4753

Abstract

Abstract : Direct design of low temperature oxidative coupling of methane catalysts is proposed 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.