Bandgap prediction of binary compounds via a machine learning approach utilizing gradient boosting regression

This study developed a predictive model for the bandgap of binary materials using a machine learning approach based on the Gradient Boosting Regressor (GBR). The model showed exceptional performance on the training set, achieving a Mean Absolute Error (MAE) of 0.087, a Root Mean Squared Error (RMSE) of 0.118, and an R² of 98%. While the model’s performance on the unseen test data was lower, with an R² of 76%, this still indicates a strong capability to predict bandgaps, explaining over 76% of the variation. The performance drop suggests a minor degree of overfitting to the training data. Visualization of the results shows that while most predictions align closely with the ideal reference line, a few outliers lead to significant errors for certain materials. This is likely because compositional features alone are insufficient to capture the intricate physical properties governing these materials.