The presence of a dataset that covers a parametric space of materials and process conditions in a process-consistent manner is essential for the realization of catalyst informatics. Here, an important piece of progress is demonstrated for the oxidative coupling of methane. A high-throughput screening instrument is developed for enabling an automatic performance evaluation of 20 catalysts in 216 reaction conditions. This affords an oxidative coupling of methane dataset comprised of 12 708 data points for 59 catalysts in three successive operations. Based on a variety of data visualization analysis, important insights into catalysis and catalyst design are successfully extracted. In particular, the simultaneous optimization of the catalyst and reactor design is found to be essential for improving the C2 yield. The consistent dataset allows the accurate prediction of the C2 yield with the aid of nonlinear supervised machine learning.

Catalysis research is on the verge of experiencing a paradigm shift regarding how catalysts are designed and characterized due to the rise of catalyst informatics. The details of catalyst informatics are reviewed where the following three key concepts are proposed: catalyst data, catalyst data to catalyst design via data science, and catalyst platform. Additionally, progress and opportunities within catalyst informatics are explored and introduced. If the field of catalyst informatics grows in the appropriate manner, the methods and approaches taken for catalyst design would be fundamentally altered, leading towards great advancement within catalysis research.

Combinatorial catalyst design is hardly generalizable, and the empirical aspect of the research has biased the literature data toward accidentally found combinations. Here, 300 quaternary solid catalysts are randomly sampled from a materials space consisting of 36,540 catalysts, and their performance in the oxidative coupling of methane is evaluated by a high-throughput screening instrument. The obtained bias-free data set is analyzed to withdraw catalyst design guidelines. Even with random sampling, 51 catalysts out of the 300 provide a C 2 yield sufficiently superior to the noncatalytic free radical process. Data analysis suggests the significance of choosing synergistic combinations, and such combinations could be generalized based on the group in the periodic table. Decision tree classification is successfully implemented to facilitate efficient sampling of quaternary catalysts toward a better C 2 yield.

Understanding the unique features of catalysts is a complex matter as it requires quantitative analysis with a relatively large selection of catalyst data.Here, unique features of each catalyst within the oxidative methane of coupling (OCM) reaction are investigated by combining data science and high throughput experimental data. Visualization of high-throughput OCM data reveals that there are several groups of catalysts based on their response against experimental conditions. Unsupervised machine learning, in particular, the Gaussian mixture model, classifies the OCM catalysts into six groups based on similarity in catalytic activities. Data visualization and parallel coordinates unveil the unique catalytic features of each classified group. Each classified group is statistically analyzed where unique features of each group are defined in term of C 2 selectivity, CH 4 conversion, and their composition in each calssified group. Thus, systematic design of catalysts can be achieved in principle on the basis of the unique features of catalysts uncovered via data science.

Catalyst data created through high-throughput experimentation is transformed into catalyst knowledge networks, leading to a new method of catalyst design where successfully designed catalysts result in high C2 yields during the OCM reaction.