Predicting Ruthenium Catalysed Hydrogenation of Esters Using Machine Learning (dataset)

Challenger Mishra (Contributor)
Niklas von Wolff (Contributor)
Abhinav Tripathi (Creator)
Claire Nicole Brodie (Contributor)
Neil Lawrence (Contributor)
Aditya Ravuri (Contributor)
Eric Brémond (Contributor)
Annika Preiss (Contributor)
Amit Kumar (Creator)

School of Chemistry

Dataset

Date made available	9 May 2023
Publisher	University of St Andrews

Contact

[email protected]

DOI
10.17630/0052eb13-a2d1-4d7a-9485-d5b2e247e63d

Access Dataset

Digital_dscovery_raw_data_2.zip
File: application/zip, 5.73 MB
Type: Dataset
Licence: CC BY

1 Finished
1 Active

New Directions for the Sythesis: New Directions for the Synthesis and Degradation of Renewable and Recyclable Plastics Using Homogeneous Catalytic (De)hydrogenation
Kumar, A. (PI)
Medical Research Council
1/08/22 → 31/07/26
Project: Fellowship
New Directions in Catalysis: New Directions in Catalysis for Sustainable Organic synthesis and Energy Storage
Kumar, A. (PI)
The Leverhulme Trust
1/01/20 → 31/07/22
Project: Fellowship

1 Article

Predicting ruthenium catalysed hydrogenation of esters using machine learning
Mishra, C., Wolff, N. V., Tripathi, A., Brodie, C. N., Lawrence, N. D., Ravuri, A., Brémond, É., Preiss, A. & Kumar, A., 1 Jun 2023, In: Digital Discovery. 2, 3, p. 819-827 9 p.
Research output: Contribution to journal › Article › peer-review

Open Access
File

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