Current Outlooks on Machine Learning Methods for the Development of Industrial Homogeneous Catalytic Systems

Abstract

This brief perspective outlines the pivotal role of Machine Learning methods in the green, digital transition of industrial chemistry. The focus on homogenous catalysis highlights the recent methodologies in the development of industrial processes, including the design of new catalysts and the enhancement of sustainable reaction conditions to lower production costs. We report several examples of Machine Learning assisted methodologies through recent Data Science trends on innovation of industrial homogeneous organocatalytic systems. We also stress the current benefits, drawbacks, and limitations towards the mass implementation of these Data Science methodologies.