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

06 April 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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 enhancement of sustainable reaction conditions to lower production costs. We report several examples of Machine Learning assisted methodologies with the 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.


Machine Learning
Homogeneous Catalysis
Catalyst Design
Data Science


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