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Unveiling gas‐phase oxidative coupling of methane via data analysis
Author(s) -
Ishioka Sora,
Miyazato Itsuki,
Takahashi Lauren,
Nguyen Thanh Nhat,
Taniike Toshiaki,
Takahashi Keisuke
Publication year - 2021
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26554
Subject(s) - oxidative coupling of methane , pairwise comparison , reaction mechanism , methane , catalysis , coupling (piping) , computer science , visualization , chemistry , informatics , coupling reaction , materials science , data mining , artificial intelligence , engineering , organic chemistry , electrical engineering , metallurgy
Unveiling the details of the mechanisms of a chemical reaction is a difficult task as reaction mechanisms are strongly coupled with reaction conditions. Here, catalysts informatics combined with high‐throughput experimental data is implemented to understand the oxidative coupling of methane (OCM) reaction. In particular, pairwise correlation and data visualization are performed to reveal the relation between reaction conditions and selectivity/conversion. In addition, machine learning is used to fill the gap between experimental data points; thus, a more detailed understanding of the OCM reaction against reaction conditions can be achieved. Therefore, catalysts informatics is proposed for understanding the details of the reaction mechanism, thereby aiding reaction design.