Predicting ruthenium catalysed hydrogenation of esters using machine learning-Reference-Cited by-同舟云学术

Predicting ruthenium catalysed hydrogenation of esters using machine learning

Published:2023 Issue:3 Volume:2 Page:819-827
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Mishra Challenger¹,von Wolff Niklas²^ORCID,Tripathi Abhinav³,Brodie Claire N.³^ORCID,Lawrence Neil D.¹,Ravuri Aditya¹,Brémond Éric⁴^ORCID,Preiss Annika³,Kumar Amit³^ORCID

Affiliation:

1. Department of Computer Science and Technology, University of Cambridge, Cambridge, CB30FD, UK

2. Laboratoire d'Electrochimie Moléculaire, Université Paris Cité, CNRS, F-75006 Paris, France

3. School of Chemistry, University of St. Andrews, St. Andrews, KY169ST, UK

4. ITODYS, Université Paris Cité, CNRS, F-75006 Paris, France

Abstract

The report describes the application of machine learning tools to predict hydrogenation of esters using molecular catalysts based on ruthenium.

Funder

Leverhulme Trust

UK Research and Innovation

University of Cambridge

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00029J

Reference44 articles.

1. Catalytic Hydrogenation of Carboxylic Acid Esters, Amides, and Nitriles with Homogeneous Catalysts

2. Homogeneous (De)hydrogenative Catalysis for Circular Chemistry – Using Waste as a Resource

3. Recent developments in the homogeneous hydrogenation of carboxylic acid esters

4. Machine Learning for Catalysis Informatics: Recent Applications and Prospects

5. Machine learning in catalysis

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hidden descriptors: Using statistical treatments to generate better descriptor sets;Artificial Intelligence Chemistry;2024-06

2. Machine Learning Models for Predicting Zirconocene Properties and Barriers;Journal of Chemical Information and Modeling;2024-01-23