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

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.

本研究旨在探究能否借助机器学习技术，加速筛选出用于铑基烯烃氢化反应的最优手性配体。本数据集包含表格数据、带分析脚本的Jupyter笔记本、交互式可视化图表以及密度泛函理论（DFT）数据。各文件夹的具体内容说明可参见项目自述文件（readme）。此外，本研究所用的机器学习流水线可于https://github.com/EPiCs-group/obelix-ml-pipeline获取，而用于催化剂结构特征化的OBeLiX工作流则可从https://github.com/EPiCs-group/obelix下载。