AI-enhanced High Resolution Functional Imaging Reveals Trap States and Charge Carrier Recombination Pathways in Perovskite

Abstract

Understanding and controlling charge carrier recombination dynamics is essential for enhancing the performance of metal halide perovskite optoelectronic devices. In this study, we present a machine learning-assisted intensity-modulated two-photon photoluminescence microscopy (ML-IM2PM) method to quantitatively map recombination processes in MAPbBr3 perovskite microcrystalline films at micrometer-scale resolution. To improve model accuracy, we implemented a balanced classification sampling strategy during the machine learning optimization phase. The resulting regression chain model effectively predicts key physical parameters across a 576-pixel spatial map, including exciton generation rate (G), initial trap concentration (N_TR), and trap energy barrier (E_a). These extracted parameters were subsequently used to solve a system of coupled ordinary differential equations, enabling spatially resolved simulations of carrier populations and recombination dynamics under steady-state photoexcitation. The simulations reveal significant spatial heterogeneity in exciton, electron, hole, and trap populations, along with photoluminescence and nonradiative losses. Correlation analysis delineates three distinct recombination regimes: (i) a trap-filling regime dominated by nonradiative recombination, (ii) a transitional crossover regime, and (iii) a band-filling regime characterized by markedly enhanced radiative efficiency. A critical trap density threshold of approximately 10^17 cm^-3 marks the transition between these regimes. Overall, this work establishes ML-IM2PM as a robust framework for probing carrier dynamics and informing defect passivation strategies in perovskite materials.