Machine learning the band gap properties of kesterite I$_2$-II-IV-V$_4$ quaternary compounds for photovoltaics applications

TL;DR

This study combines first-principles calculations and machine learning to accurately predict the band gap properties of kesterite I$_2$-II-IV-V$_4$ compounds, identifying promising solar absorber materials.

Contribution

It introduces machine learning models, especially support-vector regression and logistic regression, to predict band gap magnitude and nature with high accuracy, enabling rapid screening of new photovoltaic materials.

Findings

Achieved a root mean squared error of 283 meV in band gap prediction.

Predicted 717 compounds with suitable band gaps for solar applications.

Identified 25 synthesizable compounds with optimal band gaps in the 1.2-1.8 eV range.

Abstract

Kesterite I$_2$-II-IV-V$_4$ semiconductors are promising solar absorbers for photovoltaics applications. The band gap and it's character, either direct or indirect, are fundamental properties determining photovoltaic-device efficiency. We use a combination of accurate first-principles calculations and machine learning to predict the properties of the band gap for a large number of kesterite I$_2$-II-IV-V$_4$ semiconductors. In determining the magnitude of the fundamental gap, we compare results for a number of machine-learning models, and achieve a root mean squared error as low as 283 meV; the best results are achieved using support-vector regression with a radial-bias kernel. This error is well within the uncertainty of even the most advanced first-principles methods for calculating semiconductor band gaps. Predicting the direct--indirect property of the band gap is more challenging. After significant feature engineering, we are able to train a classifier that predicts the nature of the band gap with an accuracy of 89 \% using logistic regression. Using these trained models, the band gap properties of 1568 kesterite I$_2$-II-IV-V$_4$ compounds are predicted. We find 717 compounds with band gaps in the range 0.5 -- 2.5 eV that can potentially act as solar absorbers, and 242 materials with a band gap in the ``\emph{optimum range}" of 1.2 -- 1.8 eV. The stability of these 242 compounds is assessed by calculating the Energy Above Hull using the Materials Project database, and the band gaps are verified using hybrid functional calculations; in the end, we identify 25 compounds that are expected to be synthesizable, and have a band gap in the range 1.2 -- 1.8 eV -- most of which are previously unexplored. These results will be useful in the materials engineering of efficient photovoltaic devices.