Knowledge extraction in catalysis utilizing design of experiments and machine learning

Abstract

In this review, we highlight how design of experiments and machine learning can be utilized in catalysis to help optimize reaction conditions, catalysts, and predict new catalyst formulations. An overview of how the techniques work is presented, and the advantages and disadvantages of the techniques are discussed. We showcase the ability to extract meaningful knowledge utilizing small experimental data sets and the recent advancements in the use of machine learning in catalysis. We conclude the review by presenting a potential method to combine the benefits of both machine learning and design of experiments to help accelerate catalyst discovery and optimization.