Identifying details of chemical reactions is a challenging matter for both experiments and computations. Here, the reaction pathway in oxidative coupling of methane (OCM) is investigated using a series of experimental data and data science techniques in which data are analyzed using a variety of visualization techniques. Data visualization, pairwise correlation, and machine learning unveil the relationships between experimental conditions and the selectivities of CO, CO2, C2H4, C2H6, and H2 in the OCM reaction. More importantly, the reaction network for the OCM reaction is constructed on the basis of the scores provided by machine learning and experimental data. In particular, the proposed reaction map not only contains the chemical compound but also contains experimental conditions. Thus, data-driven identification of chemical reactions can be achieved in principle via a series of experimental data, leading to more efficient experimental design and catalyst development.

中文翻译：

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甲烷驱动的氧化偶联反应中反应网络的数据驱动识别实验数据。

对于实验和计算而言，识别化学反应的细节都是具有挑战性的事情。在此，使用一系列实验数据和数据科学技术研究了甲烷氧化偶联（OCM）中的反应途径，其中使用各种可视化技术分析了数据。数据可视化，成对相关和机器学习揭示了OCM反应中实验条件与CO，CO2，C2H4，C2H6和H2选择性之间的关系。更重要的是，OCM反应的反应网络是基于机器学习和实验数据提供的分数构建的。特别地，提出的反应图不仅包含化合物，而且包含实验条件。从而，