Data-Driven Design and Understanding of Noble Metal-Based Water-Gas Shift Catalysts from Literature Data

Catalyst informatics and catalyst design have the potential to facilitate and speed up catalyst discovery, as this is a complex undertaking involving variables associated with the catalysts themselves and operating conditions. Herein, a Machine Learning (ML)-assisted methodology coupled with data visualization to design descriptors for catalyst materials are proposed using a previously reported literature data set of the Water-Gas Shift (WGS) reaction. This entails two different approaches to represent catalysts as part of the input and propose catalysts based on their predicted CO conversion. The analysis covers the design of the descriptors employed by the models, as well as the results of an inverse prediction, that uncovered potential catalysts that can be researched for high CO conversion (>= 95%), with Random Forest Regression predicting promoted Au/CeO2-ZrO2 and Support-Vector Regression predicting promoted Au/CeO2, Ru/CeO2, and Rh/CeO2 as the best overall catalyst candidates, and Yb/Au/CeO2-ZrO2 to be of interest for WGS applications at low temperatures.