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Front Cover: Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data (ChemCatChem 15/2018)
Author(s) -
Takahashi Keisuke,
Miyazato Itsuki,
Nishimura Shun,
Ohyama Junya
Publication year - 2018
Publication title -
chemcatchem
Language(s) - English
Resource type - Reports
SCImago Journal Rank - 1.497
H-Index - 106
eISSN - 1867-3899
pISSN - 1867-3880
DOI - 10.1002/cctc.201801203
Subject(s) - front cover , catalysis , oxidative coupling of methane , methane , cover (algebra) , coupling (piping) , yield (engineering) , chemistry , front (military) , machine learning , artificial intelligence , computer science , materials science , organic chemistry , engineering , mechanical engineering , composite material
The Front Cover shows machine learning as a tool for catalyst design based on experimental catalysis data. In their Full Paper, K. Takahashi et al. explored catalysts for oxidative coupling of methane (OCM) using machine learning and literature data. Machine learning revealed the descriptors responsible for determining the C2 yield produced during the OCM reaction. Trained machine predicts 56 undiscovered effective catalysts, which were evaluated by first principle calculations that confirmed CH 4 and O 2 activation. More information can be found in the Full Paper by K. Takahashi et al on page 3223 in Issue 15, 2018 (DOI: 10.1002/cctc.201800310).