ML-GWBSE

Python codes to develop ML models using our GW-BSE database (https://hydrogen.cmd.lab.asu.edu/) and applying it with Materials Project (https://next-gen.materialsproject.org/) data.

Description of files:

1. funcs.py: All the internal functions are defined here.
2. prep_mydata.py: Code to retrieve data from computed GW-BSE database
3. prep_mpdata.py: Code to retrieve data from the Materials Project database using MP API-key
4. ml_regression.py: Code to develop and use the regression ML models
5. ml_classification.py: Code to develop and use the classification ML models
6. check_conv.py: Example of using ML regression routines to check convergence with several parameters
7. classify.py: Example of using ML classification routines to use different classification cutoff parameters
8. comp_anlz.py: Code to analyze the starting dataset to classify them based on spacegroup etc.

Citation

If you use these codes, please cite

1. T. Biswas, and A. K. Singh. "Incorporating quasiparticle and excitonic properties into material discovery." arXiv preprint arXiv:2401.17831 (2024).
2. T. Biswas, and A. K. Singh. "py GWBSE: a high throughput workflow package for GW-BSE calculations." npj Computational Materials 9.1, 22 (2023).