2025/04/29 by Qi Shi, Shi, Qi, Tõnu Pullerits +1
Engineering · Materials Science · #Chemical Physics (physics.chem-ph) #FOS: Physical sciences #Heusler alloys: electronic and magnetic properties #Machine Learning in Materials Science #Perovskite Materials and Applications
paper · pdf · doi:10.48550/arxiv.2504.20826
openalex publication_date 2025/04/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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 (NTR), and trap energy barrier (Ea). 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 1017 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.