Data underlying the publication: Probing Machine Learning Models Based on High-Throughput Experimentation Data for the Discovery of Asymmetric Hydrogenation Catalysts

DOI:10.4121/ecbd4b91-c434-4bdf-a0ed-4e9e0fb05e94.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/ecbd4b91-c434-4bdf-a0ed-4e9e0fb05e94

Datacite citation style

Kalikadien, Adarsh V.; Valsecchi, Cecile; van Putten, Robbert; Maes, Tor; Muuronen, Mikko et. al. (2024): Data underlying the publication: Probing Machine Learning Models Based on High-Throughput Experimentation Data for the Discovery of Asymmetric Hydrogenation Catalysts. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/ecbd4b91-c434-4bdf-a0ed-4e9e0fb05e94.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

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.

History

  • 2024-07-18 first online, published, posted

Publisher

4TU.ResearchData

Format

tabular data/.xlsx, jupyter notebooks/.ipynb, interactive figures/.html, DFT input/(.xyz or .inp), DFT output/.log, Python objects with prediction results/.pkl

Organizations

TU Delft, Faculty of Applied Sciences, Department of Chemical Engineering

DATA

Files (16)