SAF CAF classification performance

This repository contains model performance on crystal structure classification for binary compounds, derived from 1,400 .cif files using features generated with SAF and CAF.

How to reproduce

```bash

Download the repository

git clone https://www.github.com/bobleesj/CAFSAFperfomance

Enter the folder

cd CAFSAFperfomance ```

Install packages listed in requirements.txt:

bash pip install -r requirements.txt

Or you may install all packages at once:

bash pip install matplotlib scikit-learn pandas CBFV numpy

To reproduce results

Run python main.py

``` imac@imacs-iMac digitial-discovery % python main.py

Processing outputs/CAF/featuresbinary.csv with 133 features (1/7). (1/4) Running SVM model... (2/4) Running PLSDA n=2... (3/4) Running PLS_DA model with the best n... (4/4) Running XGBoost model... ===========Elapsed time: 8.30 seconds===========

...

Processing outputs/CBFV/oliynyk.csv with 308 features (7/7). (1/4) Running SVM model... (2/4) Running PLSDA n=2... (3/4) Running PLSDA model with the best n... (4/4) Running XGBoost model... ===========Elapsed time: 12.88 seconds=========== imac@imacs-iMac digitial-discovery % ```

Check the outputs folder for ML reports, plots, etc.

For Figures 8 and 9, run: python figure_8_case_study.py and python figure_9_case_study.py

Result

Our SAF+CAF features does a great job with classifying crystal structuree for intermetallic binary compouds.

This a PLS-DA Component N=2 result for crystal structure that you can find under outputs/SAF_CAF/PLS_DA_plot

• Compositional features were created using CAF. Ex) outputs/CAF/features_binary.csv
• Structural features were created using SAF Ex) outputs/SAF/binary_features.csv

To customize for your data

1. Place a features.csv file in the data folder. It should have a "Structure" column, from which we'll extract all "y" values.
2. Place a CSV file with features in a subdirectory within outputs. Example: outputs/SAF_CAF/binary_features.csv

To format the code

To automatically format Python code and organize imports:

bash black -l 79 . && isort .

To generate features with CBFV

Run the following command:

bash python featurizer.py

Questions?

For help with generating structural data using SAF, contact Bob at sl5400@columbia.edu.