Understanding and managing charge carrier recombination dynamics is crucial for optimizing the performance of metal halide perovskite optoelectronic devices. In this work, we introduce a machine learning-assisted intensity-modulated two-photon photoluminescence microscopy approach for quantitatively mapping recombination processes in MAPbBr₃ perovskite microcrystalline films at micrometer-scale resolution. To enhance model accuracy, a balanced classification sampling strategy was applied during the machine learning optimization stage. The trained regression chain model accurately predicts key physical parameters—exciton generation rate (G), initial trap concentration (N_TR), and trap energy barrier (E_a)—across a 576-pixel spatial mapping. These parameters were then used to solve a system of coupled ordinary differential equations, yielding spatially resolved simulations of carrier populations and recombination behaviors at steady-state photoexcitation. The resulting maps reveal pronounced local variations in exciton, electron, hole, and trap populations, as well as photoluminescence and nonradiative losses. Correlation analysis identifies three distinct recombination regimes: 1) a trap-filling regime predominated by nonradiative recombination, 2) a crossover regime, and 3) a band-filling regime with significantly enhanced radiative efficiency. A critical trap density threshold (~10¹⁷ cm^-3) marks the transition between these regimes. This work demonstrates machine learning-assisted intensity-modulated two-photon photoluminescence microscopy as a powerful framework for diagnosing carrier dynamics and guiding defect passivation strategies in perovskite materials.

Colloidal semiconductor nanocrystals, referred to as quantum dots, offer simple low-temperature solution-based methods for constructing optoelectronic devices such as light emitting diodes and solar cells. We review recent progress in the understanding of photoinduced processes in key components of a certain type of quantum dot solar cells where the dots sensitize a suitable metal oxide, such as ZnO or TiO₂, for electron injection, and NiO for hole injection. The electron and hole injection dynamics are discussed in detail as a function of the quantum dot size and core-shell structure, the linker molecule type, and the morphology of the accepting metal oxide. Hole trapping is identified as a major factor limiting the performance of quantum dot-based devices. We review possible strategies for improvement that use core-shell structures and directed excitation energy transfer between quantum dots. Finally, the generation and injection of multiple excitons are revisited. We show that the assumption of a linear relationship between the intensity of transient absorption signal and the number of excitons does not generally hold, and this observation can partially explain highly disparate results for the efficiency of generating multiple excitons. A consistent calculation procedure for studies of multiple exciton generation is provided. Finally, we offer a brief personal outlook on the topic.