Machine Learning Analysis of Literature Data on the Water Gas Shift Reaction toward Extrapolative Prediction of Novel Catalysts
Shinya Mine, Yuan Jing, Takumi Mukaiyama, Motoshi Takao, Zen Maeno, Ken‐ichi Shimizu, Ichigaku Takigawa, Takashi Toyao
Abstract
Abstract Literature data based on the water gas shift (WGS) reaction have been analyzed using statistical methods based on machine learning (ML). Our ML approach, which considers elemental features as input representations rather than the catalyst compositions, was successfully applied, and new promising catalyst candidates for future research were proposed.
Topics & Concepts
Water-gas shift reactionChemistryCatalysisStatistical learningStatistical analysisArtificial intelligenceComputer scienceOrganic chemistryStatisticsMathematicsMachine Learning in Materials ScienceCatalysis and Oxidation ReactionsComputational Drug Discovery Methods