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 intens...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 MAPbBr_(3) perovskite microcrystalline films at micrometer-scale resolution.To enhance model accuracy,a balanced classification sampling strategy was applied during the machine learning optimization stage.展开更多
基金financial support from Swedish Energy Agency grant 50709-1Swedish Research Council grant 2021-05207+2 种基金KAW WISE/WASP grant 01-22Olle Engkvist foundation grant 235-0422Light and Materials profile area at Lund University(Young Investigator Synergy Award,2023)。
文摘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 MAPbBr_(3) perovskite microcrystalline films at micrometer-scale resolution.To enhance model accuracy,a balanced classification sampling strategy was applied during the machine learning optimization stage.
