Machine learning study for perovskite solar cell

Perovskites solar cells, based on organic-inorganic lead halide-based materials (MA)PbI$_3$ (MA=CH$_3$NH$_3^+$), have shown great potential in achieving high power-conversion efficiencies and low production cost in recent years. However, this type of material usually suffers from a stability issue due to the volatility of the organic MA cation, and the toxicity and environmental mobility of ionic Pb2+ are concerns for their large-scale implementation. In our study, we use artificial intelligence and machine learning to assist the design of perovskite solar cell materials. Cutting-edge neural network based deep learning algorithms, combined with large-scale dataset obtained from first-principles numerical calculations, will greatly accelerate the design process by effectively revealing the optimized materials parameters.