Data underlying the publication: Data-driven virtual screening of conformational ensembles of transition-metal complexes

In this research we explored data-driven filtering approaches to connect CREST-generated conformer ensembles to their DFT-optimized counterparts, enabling automated conformer selection in the screening of Rh-based catalysts. The dataset contains the input and output for DFT optimization performed with Gaussian 16, next to the input and output of conformer searching done via the open-source CREST tool. Additionally, the dataset contains the data and code needed to reproduce each analysis and figure as shown in the publication. Additional figures that were not included in the main text are added to this dataset. Specific details on what each folder contains can be found in the readme.

本研究探索了数据驱动的过滤方法，用于将CREST工具生成的构象系综（conformer ensemble）与经密度泛函理论（Density Functional Theory, DFT）优化的对应构象系综进行关联，从而实现铑基催化剂筛选流程中的自动化构象选择。
本数据集包含通过高斯16（Gaussian 16）完成的DFT优化任务的输入与输出文件，以及通过开源CREST工具进行构象搜索得到的输入与输出文件。
此外，本数据集还包含复现论文中各项分析与图表所需的数据与代码。未纳入论文正文的额外图表已一并收录至本数据集。各文件夹的具体内容说明可在自述文件（README）中查阅。