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Unveiling Hidden Catalysts for the Oxidative Coupling of Methane based on Combining Machine Learning with Literature Data
Author(s) -
Takahashi Keisuke,
Miyazato Itsuki,
Nishimura Shun,
Ohyama Junya
Publication year - 2018
Publication title -
chemcatchem
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.497
H-Index - 106
eISSN - 1867-3899
pISSN - 1867-3880
DOI - 10.1002/cctc.201800310
Subject(s) - catalysis , oxidative coupling of methane , yield (engineering) , methane , chemistry , process (computing) , coupling (piping) , computer science , computational chemistry , materials science , thermodynamics , organic chemistry , physics , metallurgy , operating system
Catalysts for oxidative coupling of methane (OCM) are explored using data science and 1868 OCM catalysts from literature data. Machine learning reveals the descriptors responsible for determining the C 2 yield produced during the OCM reaction. Trained machine predicts 56 undiscovered catalysts with corresponding conditions for OCM reactions achieving a C2 yield over 30 %. First principle calculations are implemented to evaluate the predicted catalysts where the activation of CH 4 , CH 3 , and O 2 are confirmed with the predicted catalysts for the OCM reaction. Thus, machine learning is proven to be an effective approach for discovering hidden catalysts and should accelerate the catalyst design process in general.