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Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning
Author(s) -
Sugiyama Kanami,
Nguyen Thanh Nhat,
Nakanowatari Sunao,
Miyazato Itsuki,
Taniike Toshiaki,
Takahashi Keisuke
Publication year - 2021
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.202001680
Subject(s) - catalysis , yield (engineering) , throughput , oxidative coupling of methane , materials science , methane , chemistry , chemical engineering , nanotechnology , computer science , organic chemistry , metallurgy , engineering , telecommunications , wireless
The combination of deep learning and high‐throughput experiments is proposed for the direct design of heterogeneous catalysts in the oxidative coupling of methane (OCM) reaction. Deep learning predicts 20 active catalysts from high‐throughput 12,708 OCM experimental data where 19 of the predicted 20 catalysts have not been previously reported. The predicted 20 catalysts are then evaluated through high‐throughput experiments where a highly active unreported catalyst Ti−Na 2 WO 4 /TiO 2 is discovered. Ti−Na 2 WO 4 /TiO 2 results in a high C 2 yield of 18.8 % where the maximum C 2 yield is reported to be approximately 20 % within the 12,708 OCM data. Furthermore, the experimental conditions predicted for Ti−Na 2 WO 4 /TiO 2 are also reproduced by high‐throughput experiment. Thus, deep learning demonstrates that both catalysts and experimental conditions can be simultaneously explored for designing catalysts. More importantly, deep learning assisted catalysts search is found to dramatically increase the chances of finding active catalysts where 9 out of 20 predicted catalysts result in a C 2 yield over 15 %. Therefore, the combination of deep learning with high‐throughput experiments is proposed to be an effective strategy for the direct design of catalysts.