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Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts
The Journal of Physical Chemistry C ( IF 3.3 ) Pub Date : 2024-12-19 , DOI: 10.1021/acs.jpcc.4c07397 Yoshiki Hasukawa, Fernando Garcia-Escobar, Keisuke Takahashi, Lauren Takahashi
The Journal of Physical Chemistry C ( IF 3.3 ) Pub Date : 2024-12-19 , DOI: 10.1021/acs.jpcc.4c07397 Yoshiki Hasukawa, Fernando Garcia-Escobar, Keisuke Takahashi, Lauren Takahashi
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The oxidative coupling of methane reaction is important in the chemical industry from the perspective of an energy source. Although it is difficult to increase C2 yield due to the stability of methane and formation of COx byproducts at high temperatures, informatics has been proven to be a useful approach for effective OCM catalyst design. However, informatics using machine learning in complex environments, such as heterogeneous catalysts, has a black box problem. Therefore, graph theory is applied to the construction of complex networks in order to clarify the relationships between multiple variables and design catalysts based on the analysis of constructed networks. All proposed catalysts are demonstrated to be active in experiments, indicating that catalyst design based on the constructed network is useful and applicable to complex reaction systems.
更新日期：2024-12-20
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