%0 Generic
%A Kalikadien, Adarsh V.
%A Valsecchi, Cecile
%A van Putten, Robbert
%A Maes, Tor
%A Muuronen, Mikko
%A Dyubankova, Natalia
%A Lefort, Laurent
%A Pidko, Evgeny
%D 2024
%T Data underlying the publication: Probing Machine Learning Models Based on High-Throughput Experimentation Data for the Discovery of Asymmetric Hydrogenation Catalysts
%U 
%R 10.4121/ecbd4b91-c434-4bdf-a0ed-4e9e0fb05e94.v1
%K Catalysis
%K Hydrogenation
%K Organometallics
%K High-throughput experimentation
%K Machine learning
%K Data science
%X <p>In this study, we investigated whether machine learning techniques could be used to accelerate the identification of the most efficient chiral ligand for Rh-based hydrogenation of olefins. The dataset contains tabular data, jupyter notebooks with analysis, interactive figures and DFT data. Specific details on what each folder contains can be found in the readme. Additionally, our machine learning pipeline can be found at https://github.com/EPiCs-group/obelix-ml-pipeline and the OBeLiX workflow to featurize the catalyst structures can be found at https://github.com/EPiCs-group/obelix.</p>
%I 4TU.ResearchData