Machine learning accelerates discovery of solar-cell perovskites

Through the generation of a dataset of accurate band gaps for perovskite materials and the use of machine learning methods, several promising halide perovskites are identified for photovoltaic applications. As we integrate solar energy into our daily lives, it has become important to find materials that efficiently convert sunlight into electricity. While silicon has dominated solar technology so far, there is also a steady turn towards materials known as perovskites due to their lower costs and simpler manufacturing processes. The challenge, however, has been to find perovskites with the right "band gap": a specific energy range that determines how efficiently a material can absorb sunlight and convert it into electricity without losing it as heat. Now, an EPFL research project led by Haiyuan Wang and Alfredo Pasquarello, with collaborators in Shanghai and in Louvain-La-Neuve, have developed a method that combines advanced computational techniques with machine-learning to search for optimal perovskite materials for photovoltaic applications.