This repo contains code, data and jupyter notebook related to RAPID.
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The following sections indicate the folders which contain code and related data
- RAPID.ipynb - Notebook containing all visualizations
- Visualization and Statistical Analysis.ipynb - Initial analysis notebook
- Perovskite learning curve.ipynb - ML code and visualizations notebook
All raw data files are located in the data
folder
- cifs - Contains the Crystallographic Information Files
- images - Contains side vial images of each experiment performed
- xrd/xy - Contains xy files for XRD data
- 0042.perovskitedata_RAPID.csv - Escalate generated data file including 8 experimental features (with "rxn" as header prefix) and 67 chemical features (with "feat" as header prefix). The detailed explanations of these features are listed in "Explanation of Features-Descriptors" section in "Perovskite Dataset Description.pdf". This CSV file is used in visualization and machine learning.
- 0042.perovskitedata_RAPID_full.csv - This escalate generated data file contains the same experiments as "0042.perovskitedata_RAPID.csv" but has all 787 features, including additional "raw" features describing experiment details (see "Explanation of Features-Descriptors" section in "Perovskite Dataset Description" for the explanations of "raw" prefix). The csv file is not used for visualization or machine learning.
- image_list.json - Keeps track of all image files in the image folder
- ml_data.pkl - Python pickle file containing ML results
- inventory.csv - Chemical inventory data
- organic_inchikey.csv - Inchi keys and chemical names
- s_spaces.json - Co-ordinates of state space for each amine
The following python scripts are used in the RAPID.ipynb notebook to generate visualizations
- plots.py - Generates the reaction outcomes 3D plot widget
- xrd_plot.py - Generates the xrd plot widget
- ml_section.py - Generates machine learning outcomes widget
- cif_plots.py - Generates the cif plot widget. Note that jsmol is used to create the widget