Direct Design of Catalysts in Oxidative Coupling of Methane via High‐Throughput Experiment and Deep Learning. Issue 3 (16th December 2020)

• Record Type: Journal Article
• Authors: Sugiyama, Kanami; Nguyen, Thanh Nhat; Nakanowatari, Sunao; Miyazato, Itsuki; Taniike, Toshiaki; Takahashi, Keisuke
• Is Part Of: ChemCatChem. Volume 13:Issue 3(2021)
• Page Start: 952
• Page End: 957
• Publication Date: 2020-12-16
• Subjects: Catalysts Informatics -- High-throughput Experiment -- Machine Learning -- Oxidative Coupling of Methane; Catalysis -- Periodicals; 541.39505
• DOI: 10.1002/cctc.202001680
• Languages: English
• ISSNs: 1867-3880

Abstract

Abstract: 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−Na2 WO4 /TiO2 is discovered. Ti−Na2 WO4 /TiO2 results in a high C2 yield of 18.8 % where the maximum C2 yield is reported to be approximately 20 % within the 12, 708 OCM data. Furthermore, the experimental conditions predicted for Ti−Na2 WO4 /TiO2 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 C2 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. Abstract : Deep learning and high‐throughput experiments are combined to design catalysts for oxidative coupling of methane. Deep learning finds two highly active unreported catalysts where high‐throughput experiments are used to evaluate the catalysts' performance.