TY - DATA
T1 - Data underlying the publication: Probing Machine Learning Models Based on High-Throughput Experimentation Data for the Discovery of Asymmetric Hydrogenation Catalysts
PY - 2024/07/18
AU - Adarsh V. Kalikadien
AU - Cecile Valsecchi
AU - Robbert van Putten
AU - Tor Maes
AU - Mikko Muuronen
AU - Natalia Dyubankova
AU - Laurent Lefort
AU - Evgeny Pidko
UR - 
DO - 10.4121/ecbd4b91-c434-4bdf-a0ed-4e9e0fb05e94.v1
KW - Catalysis
KW - Hydrogenation
KW - Organometallics
KW - High-throughput experimentation
KW - Machine learning
KW - Data science
N2 - <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>
ER -