Density Functional Theory and Machine Learning for Electrochemical Square-Scheme Prediction: An Application to Quinone-type Molecules Relevant to Redox Flow Batteries



The uploaded data contains (i) "01_Data" optimized molecular structure in XYZ format and the primary attributes and SMILES, (ii) "02_Datasets" datasets used in the publication, and (iv) "03_pynb_script" a Jupyter-Notebook. The 01_Data directory contains more than 8000 subdirectories. Each is for a molecule that undergoes a two-proton two-electron transfer reaction. In each subdirectory, one finds the following files: (1) directories named corresponding to the ones in Figure 1 of the paper. Inside each, there are geometries and properties in XYZ and CSV format, respectively. (2) "freeEnergy.dat" contains the free energy of different states. (3) "schemesquare.dat" has the parameters of the electrochemical scheme of square representation. The new version (v1.1) contains some updates around: (i) The DFT calculations workflow in a folder called "04_workflow_of_DFT" The Gaussian input files have been explained in the "README" file. (ii) The Python scripts for data extraction have been added and can be found in "05_how_to_extracted_data" (iii) We explained how to compute the Purbaix diagram in great detail "06_how_to_compute_Pourbaix_diagram/" All these changes/improvements were applied/made following the Referee of Digital Discovery Journal. Here, we would like to thank him/her.
Koska saatavilla20 toukok. 2023

Dataset Licences

  • CC-BY-4.0

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