Composition and Structure Based GGA Bandgap Prediction Using Machine Learning Approach

TL;DR

This paper develops machine learning models to accurately predict GGA bandgaps from composition and structure, validated by DFT calculations, with ensemble methods outperforming standalone models.

Contribution

It introduces ensemble machine learning approaches for bandgap prediction, achieving high accuracy and validating predictions with DFT calculations for new compounds.

Findings

RF model achieved R2 of 0.943 and RMSE of 0.504 eV.

Ensemble bagging models achieved R2 of 0.948 and RMSE of 0.479 eV.

Predicted new compounds are narrow bandgap semiconductors validated by DFT.

Abstract

This study focuses on developing precise machine learning (ML) regression models for predicting energy bandgap values based on chemical compositions and crystal structures. The primary aim is to match the accuracy of predictions derived from GGA-PBE calculations and validate them through density functional theory (DFT)-based band structure calculations. We assessed eight standalone ML regression models, including AdaBoost, Bagging, CatBoost, LGBM, RF, DT, GB, and XGB. These models were analyzed for their ability to predict GGA-PBE bandgap values across diverse material structures and compositions, using a dataset containing bandgap values for 106,113 compounds. Additionally, we constructed four ensemble models using the stacking method and seven using the bagging method. These ensemble models incorporated RidgeCV and LassoCV to explore if ensemble techniques could enhance prediction accuracy. The dataset was divided into subsets of varying sizes: 10,000, 25,000, 50,000, and 100,000 entries. We determined feature importance through permutation techniques and established a correlation coefficient matrix using the Pearson correlation method. The Random Forest (RF) model emerged as the top performer among standalone models, achieving an R2 value of 0.943 and an RMSE value of 0.504 eV. Bagging regression demonstrated improved performance across different dataset sizes with streamlined feature selection. Ensemble models, particularly bagging, consistently outperformed standalone models, achieving the best R2 value of 0.948 and an RMSE value of 0.479 eV in the test dataset. Using the best-performing model, we predicted bandgap values for new half-Heusler compounds with 18 valence electron counts. These predictions were successfully validated using accurate DFT calculations. DFT calculations indicated that the newly predicted compounds are narrow bandgap semiconductors with dynamic stability.