BandGap-ml 0.4.2

Project for predicting band gaps of inorganic materials by using ML models

BandGap-ml v0.4.2

Table of Contents

• Project Description
• Prepare Workspace Environment with Conda
• Models Construction
• Usage
• Author
• License

Project Description

Project for predicting band gaps of inorganic materials by using ML models.

Try out new Frontend Web Interface running at:

bandgap-ml.vercel.app

Prepare Python Workspace Environment with Conda

1. Download Miniforge for Unix-like platforms (macOS & Linux)

# Download the installer using curl or wget or your favorite program:
curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"

# OR 
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"

# Run the script:
bash Miniforge3-$(uname)-$(uname -m).sh

and follow instructions. See the documentation for Miniforge for more information.

# 2. Create and activate the conda environment
conda create --name bandgap-ml "python<3.13"
conda activate bandgap-ml

# 3. Install BandGap-ml
# 3.1 From PyPI
pip install BandGap-ml

# 3.2 Or install the latest version from the GitHub repository
pip install git+https://github.com/alexey-krasnov/BandGap-ml.git

# 3.3 Or install the latest version in editable mode from the GitHub repository
git clone https://github.com/alexey-krasnov/BandGap-ml.git
cd BandGap-ml
pip install -e .

• Where -e means "editable" mode.

Data source

For training Random Forest Classifier and Regression models, we adopted data provided in the following paper:

• 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.

Models construction

To perform model training, validation, and testing, as well as saving your trained model, run the following command in the CLI:

python band_gap_ml/model_training.py

This command executes the training and evaluation of RandomForestClassifier and RandomForestRegressor models using the predefined paths in the module.

Usage

We provide several options to use the BandGap-ml package.

1. Jupyter Notebook

A Jupyter Notebook file in the notebooks directory provides an easy-to-use interface for training models and using them for Band Gap predictions.

2. Python Code

You can use the package directly in your Python code:

2.1 Train models

from band_gap_ml.model_training import train_and_save_models

train_and_save_models()

2.2 Make predictions of band gaps by using the BandGapPredictor class:

from band_gap_ml.band_gap_predictor import BandGapPredictor

# Initialize the predictor with default best model
predictor = BandGapPredictor()

# Or specify a different model type and path to the model
# predictor = BandGapPredictor(model_type='RandomForest', model_dir= <YOUR_PATH_TO_THE_MODEL>)
# predictor = BandGapPredictor(model_type='GradientBoosting')
# predictor = BandGapPredictor(model_type='XGBoost')

# Prediction from csv file containing chemical formulas
input_file = 'samples/to_predict.csv'
predictions_df = predictor.predict_from_file(input_file)
print(predictions_df)

# Prediction from one or multiple chemical formulas
formula_1 = 'BaLa2In2O7'
formula_2 = 'TiO2'
formula_3 = 'Bi4Ti3O12'

# Single formula prediction
single_prediction = predictor.predict_from_formula(formula_1)
print(single_prediction)

# Multiple formulas prediction
multiple_predictions = predictor.predict_from_formula([formula_1, formula_2, formula_3])
print(multiple_predictions)

# Save predictions to a CSV file
multiple_predictions.to_csv('predictions_results.csv', index=False)

3. Web Service

You can use BandGap-ml as a web service in two ways:

3.1 Use our hosted web interface at: bandgap-ml.vercel.app

3.2 Run the web service locally with Docker:

• Prerequisites
  • Docker
  • Docker Compose
  • Git
• Build and start the Docker containers

docker compose up -d --build

• Once the containers are running, you can access:

  • BandGap-ml frontend web interface in your browser at http://localhost:8080
  • Backend API: http://localhost:3000
  • API Documentation: http://localhost:3000/docs

• The application runs two main containers:

  • frontend: Vue.js application (Port 8080)
  • backend: FastAPI application running with uvicorn (Port 3000)

• To stop the containers:

docker compose down

• To check container status:

docker compose ps

• To view container logs:

# All containers
docker compose logs

# Specific container
docker compose logs frontend
docker compose logs backend

3.3 Run the backend and frontend parts of the web service separately:

• Backend

uvicorn band_gap_ml.app:app --host 127.0.0.1 --port 3000 --workers 1 --timeout-keep-alive 3600

• Frontend

cd BandGap-ml/frontend
npm run serve

Author

Dr. Aleksei Krasnov [e-mail]

Citation

• 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. https://doi.org/10.1021/acs.jpclett.8b00124

License

This project is licensed under the MIT - see the LICENSE.md file for details.