Journal
CATALYSIS SCIENCE & TECHNOLOGY
Volume 11, Issue 2, Pages 524-530Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d0cy01751e
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Funding
- Japan Science and Technology Agency (JST) CREST [JPMJCR17P2]
- JSPS KAKENHI
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By utilizing machine learning and data mining, a direct design of low temperature oxidative coupling of methane (OCM) catalysts was achieved, revealing hidden physical rules behind catalysis and leading to the discovery of new catalysts.
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.
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