Application of DFT combined with machine learning in perovskite fluorescence properties

Density functional theory is a method based on quantum mechanics. It uses charge density instead of wave function to study the electronic structure of multi-particle systems. It is an indispensable research for studying material properties in the field of material science and chemistry. However, the defect of this method is the long calculation cycle, low efficiency and high cost. Machine learning has powerful data processing capabilities and high data prediction performance, which can accelerate the research process of new materials. This paper introduces the application of density functional theory and machine learning in the calculation of perovskite fluorescent materials.