BandGapPredictor

Predict the bandgap energy for inorganic materials

This package provides a machine learning model trained based on experimetally measurements to predict the bandgap energy (Eg) for inorganic materials via the command-line.

Table of Contents

• Citations
• Prerequisites
• Usage
  • Define a customized prediction set
  • Predict bandgap energy
• Authors

Citations

To cite relative permittivity and centroid shift predictions, please reference the following work:

Zhuo. Y, Mansouri Tehrani., and Brgoch. J, Predicting the band gaps of inorganic solids by machine learning, J. Phys. Chem. Lett. 2018, 9, 1668-1673.

Prerequisites

This package requires:

• pymatgen
• scikit-learn
• pandas
• NumPy
• xlrd

Usage

Define a customized prediction set

You should create a .xlsx file named to_predict.xlsx, in which the compositions that are of interest are listed in the first column with the header "Composition". There is an example of the to_predict.xlsx file in the repository.

Predict bandgap energy

After preparing to_predict.xlsx, you can get the Eg prediction by:

python Eg_model.py

Eg_model.py will automatically read elements.xlsx and Training_Set.xlsx to generate a prediction. A classifier will first categorize a composition into metals (Eg = 0) or nonmetals (Eg > 0), then the Eg of nonmetals will be predicted with a regressor. After running, you will get a .xlsx file named predicted.xlsx in the same directory, in which the predicted Eg is provided next to the corresponding composition.

Authors

This software was created by Ya Zhuo who is advised by Prof. Jakoah Brgoch.