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.

Figure 4. Projected density of state (PDOS)ofC H 4 (left) and CH 3 (right) over (a) Na-doped V(100), (b) Cu-doped V(100),a nd (c) Ba-doped V(100).

Undiscovered perovskite materials for applications in capturing solar lights are explored through the implementation of data science. In particular, 15000 perovskite materials data is analyzed where visualization of the data reveals hidden trends and clustering of data. Random forest classification within machine learning is used in order to predict the band gap of perovskite materials where 18 physical descriptors are revealed to determine the band gap. With trained random forest, 9328 perovskite materials with potential for applications in solar cell materials are predicted. The selected Li and Na based perovskite materials within predicted 9328 perovskite materials are evaluated with first principle calculations where 11 undiscovered Li(Na) based perovskite materials fall into the ideal band gap and formation energy ranges for solar cell applications. Thus, the implementation of data science accelerates the discovery of hidden perovskite materials and the approach can be applied to the materials science in general for searching undiscovered materials.