cff-version: 1.2.0
abstract: "<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>"
authors:
  - family-names: Kalikadien
    given-names: Adarsh V.
    orcid: "https://orcid.org/0000-0002-5414-3424"
  - family-names: Valsecchi
    given-names: Cecile
    orcid: "https://orcid.org/0000-0002-5535-053X"
  - family-names: van Putten
    given-names: Robbert
    orcid: "https://orcid.org/0000-0001-5074-6706"
  - family-names: Maes
    given-names: Tor
  - family-names: Muuronen
    given-names: Mikko
    orcid: "https://orcid.org/0000-0001-9647-7070"
  - family-names: Dyubankova
    given-names: Natalia
    orcid: "https://orcid.org/0000-0002-5892-3778"
  - family-names: Lefort
    given-names: Laurent
    orcid: "https://orcid.org/0000-0003-2973-6540"
  - family-names: Pidko
    given-names: Evgeny
    orcid: "https://orcid.org/0000-0001-9242-9901"
title: "Data underlying the publication: Probing Machine Learning Models Based on High-Throughput Experimentation Data for the Discovery of Asymmetric Hydrogenation Catalysts"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/ecbd4b91-c434-4bdf-a0ed-4e9e0fb05e94.v1
license: CC BY 4.0
date-released: 2024-07-18