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.

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.

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Dynamic reconstruction under a physicochemical environment is an intrinsic property of solid surfaces, in particular when associated with catalysis on nanosized or nanostructured materials. Here, we report nonempirical structure determination of TiCl4-capped MgCl2 nanoplates that is based on the combination of a genetic algorithm and density functional calculations. The methodology for the nonempirical structure determination was developed, and its application was demonstrated for 7MgCl2, 15MgCl2, and 15MgCl2/4TiCl4 in relation to the hidden identity of primary particles of the Ziegler–Natta catalyst. Bare MgCl2 nanoplates dominantly exposed {100} surfaces at their lateral cuts, but the chemisorption of TiCl4 induced reconstruction by stabilizing {110} surfaces. The most important finding of the present research is that TiCl4 exhibited distributed adsorption states as consequences of chemisorption on nonideal finite surfaces and the diversity of thermodynamically accessible structures. The assessment of the Ti distribution is essential for the distribution of primary structures of produced polymer, and in this study, we made these determinations.

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