Article
Predicting Inorganic Photovoltaic Materials with Efficiencies >26% via Structure-Relevant Machine Learning and Density Functional Calculations

https://doi.org/10.1016/j.xcrp.2020.100179Get rights and content
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Highlights

  • Fast and atomic-level accuracy prediction of photovoltaic materials is proposed

  • The theoretical PCE exceeds 26%, comparable to the champion light-absorbing layer

Summary

Discovering new inorganic photovoltaic materials becomes an efficient way for developing a new generation of solar cells with high efficiency and environmental stability. Using machine learning (ML) and density functional theory calculations, we report four promising inorganic photovoltaic materials—Ba4Te12Ge4, Ba8P8Ge4, Sr8P8Sn4, and Y4Te4Se2—demonstrating notable theoretical photovoltaic performance for use in solar cells. The symmetry-allowed optical transition probability, the large amount of density of states near the conduction band minimum (CBM) and the valence band maximum (VBM), and the strong p-p transition across the band edge contribute to the large optical absorption coefficient, leading to the outstanding theoretical power conversion efficiency (PCE). The separation of the VBM and CBM wave function distributions contribute to the fast separation of the photogenerated electrons and holes and the enhanced carrier lifetimes. Our ML model is an efficient method for fast and atomic-level accuracy prediction of photovoltaic materials with different crystal structures.

Keywords

machine learning
density functional theory
photovoltaic materials
power conversion efficiency

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