Oxidative coupling methane (OCM) is challenging reaction as it operates at high temperature (>700 ^○C) resulting the production of CO_x becomes crucial byproduct. Furthermore, its reaction route consists of gas phase and surface reactions with complicated-stepwise procedure. Thus, controlling reaction has been attempted via catalyst in order to obtain desirable C2 production. However, catalyst design for OCM reaction has been stagnated for decades because of its complexity of reaction. On the other hand, data science approach appears to be a powerful approach to unveil the undiscovered properties of OCM reaction.Here, catalysts informatics is proposed to unveil the details mechanism of OCM reaction via experiment.In particular, multi-output machine learning successfully predicts the selectivity of CO, CO₂, C₂H₄ , C₂H₆, C₂, and CH₄ conversion instantaneously within the X-Na₂WO₄/SiO₂(X:(none), Mn, Cu, and Ti) catalysts where the change of each selectivity against experimental conditions are fully unveiled by interpolation filling. The result reveals the not only the effect of X in X-Na₂WO₄/SiO₂ catalysts, but also the trade-off relation between C₂H₆ and CO₂ selectivity, providing the guidance of both catalyst design and optimized experimental conditions. Additionally, it is discovered that the reaction network can be represented via machine learning based on the trend in experimental data. Therefore, data science technique advances the study of OCM reaction, indicating to be a promising approach for catalyst research.

Shun Nishimura

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