期刊
CHEMCATCHEM
卷 11, 期 17, 页码 4307-4313出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/cctc.201900843
关键词
Heterogeneous catalysis; machine learning; oxidative coupling of methane
资金
- Japan Science and Technology Agency (JST) CREST [JPMJCR17P2]
- JSPS KAKENHI [JP17K14803]
- Materials research by Information Integration (MI2I) Initiative project of the Support Program for Starting Up Innovation Hub from JST
The challenge in catalytic reactions lies within its complexity coming from high dimensional experimental factors. In order to solve such complexity, machine learning is implemented to treat experimental conditions in high dimensions. Oxidative coupling of methane, methane to C-2 compounds (ethylene and ethane), is chosen as the prototype reaction where 156 data consisting of various experimental conditions is prepared. Machine learning reveals that the relationship between experimental conditions and C-2 yield is non-linear matter. In particular, extreme tree regression is found to accurately reproduce the experimental data. In addition, machine learning predictions can be a good indicator for designing experiments. Thus, machine learning can be a powerful approach towards understanding and determining experimental conditions in high dimension.
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