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Predicting ruthenium catalysed hydrogenation of esters using machine learning
Paper Details
Published: 2023/11/20
Journal: Digital Discovery
DOI: /10.1039/D3DD00029J
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 bottlenecks in the commercialization of such technologies. This paper explores the approach of machine learning to predict outcomes of catalytic hydrogenation of esters using various ML architectures
Authors
Challenger Mishra
Cambridge University
Departmental Early Career Academic Fellow, Accelerate Programme
Neil D. Lawrence
Cambridge University
The DeepMind Professor of Machine Learning
Aditya Ravuri
Cambridge University
PhD Student
N. von Wolff
A. Tripathi
C.N. Brodie
E. Bremond
A. Preiss