Exploration of highly stable and highly efficient new lead-free halide perovskite solar cells by machine learning
Chenyang Zhu, Yiming Liu, Donghua Wang, Zhikang Zhu, Peng Zhou, Yibo Tu, Gaoyuan Yang, Hongyu Chen, Yue Zang, Jinxia Du, Wensheng Yan
Abstract
Density functional theory (DFT) calculations have always been an important tool for predicting and discovering new materials. Using machine learning (ML) technology and 488 datasets computed with the Perdew-Burke-Ernzerhof (PBE) functional, we constructed and trained ML models, subsequently expanding the perovskite database to 177,264 datasets. Finally, high-throughput screening technology was adopted. Through a three-stage screening process evaluating stability, band gap, and photovoltaic performance, the 177,264 datasets were reduced to 434. After high-throughput screening, the spectroscopic limited maximum efficiency (SLME) values of the remaining perovskite solar cells with practical considerations all well exceed 20%, and the selected four perovskite cells exceed 23%. The four groups of perovskite solar cells with the highest SLME values were all lead free. This study provides valuable insights for advancing the development of green lead-free perovskite solar cells with enhanced efficiency and stability.