Choice of the right supporting electrolyte in electrochemical reductions: a principal component analysis

• Main Authors: Mast, Florian, Hielscher, Maximilian M., Wirtanen, Tom, Erichsen, Max, Gauss, Jürgen, Diezemann, Gregor, Waldvogel, Siegfried R.
• Format: Other/Unknown Material
• Language: English
• Published: Zenodo 2024
• Subjects: Electrochemistry; Principal Component Analysis; Cathodic Reduction; DFT Derived Dataset; Molecular Dynamics Simulation; Orca
• Online Access: https://doi.org/10.5281/zenodo.10813969

Introduction This dataset contains the raw data as well as an HTML-based visualization of our dataset using Python Bokeh. We have also added a feature to highlight commercially available supporting electrolytes. The data is taken from the PubChem database. For each of the 6650 cations, the known neutral compounds in the PubChem dataset were identified with their corresponding anions. For each of these compounds, the vendor information stored in PubChem was queried. Directory structure Raw Data [ raw_data.tar.xz ] Compressed directory with the output from the automated feature calculation. [ raw_data.csv ] CSV file with the values of the calculated properties of all cations. Visualization [ pca_qac_tool_QC.html ] HTML page with Javascript to display PC1 and PC2 for the quantum chemical PCA model. [ pca_qac_tool_RDKit.html ] HTML page with Javascript to display PC1 and PC2 for the PCA model based on non empirical RDKit descriptors. Tools [ pca_qac_tool.py ] Python script to generate the HTML output using Bokeh. Depends on the data_pca_qac.csv and the data_commercial.json file. [ PubChem_get_Vendor_information.py ] Crawler that checks a list of PubChem CIDs for net-neutral compounds and whether they are commercially available. [ RDKit_Descriptor-2D.py ] Python script to calculate all available 2D RDkit descriptors based on a list of SMILES strings. [ RDKit_Descriptor-3D.py ] Python script to calculate the RDKit 3D descriptors based on the CREST and ORCA GeoOpt geometries.