Prediction model of band-gap for AX binary compounds by combination of density functional theory calculations and machine learning techniques

TL;DR

This study develops machine learning models combining density functional theory data to accurately predict the band-gaps of AX binary compounds, significantly improving prediction accuracy over traditional methods.

Contribution

The paper introduces a novel approach integrating DFT calculations with machine learning to predict band-gaps of AX compounds with high accuracy, surpassing previous models.

Findings

SVR model achieves RMSE of 0.18 eV

Using multiple predictors reduces prediction error

Predicted band-gaps can aid materials discovery

Abstract

Machine learning techniques are applied to make prediction models of the G0W0 band-gaps for 156 AX binary compounds using Kohn-Sham band-gaps and other fundamental information of constituent elements and crystal structure as predictors. Ordinary least square regression (OLSR), least absolute shrinkage and selection operator (LASSO) and non-linear support vector regression (SVR) methods are applied with several levels of predictor sets. When the Kohn-Sham band-gap by GGA (PBE) or modified Becke-Johnson (mBJ) is used as a single predictor, OLSR model predicts the G0W0 band-gap of a randomly selected test data with the root mean square error (RMSE) of 0.54 eV. When Kohn-Sham band gap by PBE and mBJ methods are used together with a set of various forms of predictors representing constituent elements and crystal structures, RMSE decreases significantly. The best model by SVR yields the RMSE of 0.18 eV. A large set of band-gaps estimated in this way should be useful as predictors for materials exploration.