Machine learning of kesterite materials using cost-effective hybrid density functional theory

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Kesterite materials have received much attention for earth-abundant photovoltaic applications, however, their record cell efficiency is still lower than other thin film solar cells. We performed high-throughput calculations using hybrid density functional theory to find an alternative absorber material. The atomic structures were cost-effectively optimized by employing a downsampled k-point grid for the Fock exchange potential, and therefore the electronic band gap (▪) was obtained in good agreement with the conventional hybrid calculation. Based on hybrid DFT calculations, we develop machine learning (ML) models to predict the Eg and suggest the empirical equation for Eg with the accuracy of the root-mean-square-error of 0.137 eV and R2 of 0.921. We found that the distance between group-IV and group-VI elements is important to estimate Eg. Using the developed ML models, we searched materials with predicted Eg within the optimum range of 1.0−1.5 eV and also compared their Eg with hybrid functional calculations. Based on our ML and DFT calculations, we suggest nine materials whose ML predicted and DFT calculated Eg in the optimum range for photovoltaic applications.