Predicting Ruthenium Catalysed Hydrogenation of Esters using Machine Learning

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


Catalytic hydrogenation of esters is a sustainable approach for the production of fine chemicals, and pharmaceutical drugs. However, the efficiency and cost of catalysts are often the bottlenecks in the commercialization of such technologies. The conventional approach of catalyst discovery is based on empiricism that makes the discovery process time-consuming and expensive. There is an urgent need to develop effective approaches to discover efficient catalysts for hydrogenation reactions. We demonstrate here the approach of machine learning for the prediction of out-comes for the catalytic hydrogenation of esters. Our models can predict the reaction yields with high mean accuracies of up to 91% (test set) and suggest that the use of certain chemical descriptors selectively can result in a more accurate model. Furthermore, cata-lysts and some of their corresponding descriptors can also be pre-dicted with mean accuracies of 85%, and >90%, respectively.


machine learning

Supplementary materials

Supporting information
The Supporting Information contains details on the construction of the dataset, creation of chemical descriptors, and the development of ML models.


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