Machine learning-based mapping of band gaps for metal halide perovskites

Recently, perovskite solar cells (PSCs) have received great attentions as the most promising candidate for the next generation solar cells; where the halide perovskite with ABX3 stoichiometry are playing as key components. As a key relevant parameter for various applications of perovskites, the band gap can be modified and optimized by tuning their compositions. In order to enhance the screening efficiency of perovskites with appropriate band gap range for particular applications, this work developed a mapping strategy for band gap range classification through data-driven selected features. This work demonstrates that the proposed features, and further the developed band gap maps, are able to offer a useful initial guiding principle for screenings of potential halide perovskites with fitting band gap ranges and provide opportunities for their compositional design.