Although solution processing methods provide an attractive route toward development of low-cost functional materials, these accessible fabrication approaches can engender high concentrations of microscopic structural defects that are detrimental to performance. In lead halide perovskites, structural disorder derived from solution processing has been implicated as an important determiner of photophysical properties. However, a direct correlation between the functional properties of these materials and the local crystal structure in which non-equilibrium states evolve has remained elusive, in part because structural heterogeneities occur on length scales that defy conventional characterization techniques. To address this knowledge gap, we have combined ultrafast pump–probe microscopy and electron backscattering diffraction to directly correlate charge carrier transport with the local diffraction pattern contrast, an indicator of crystal quality. Spatial correlation of these measurements strongly suggests that even on individual single crystal CsPbBr3 domains, microscopic variability in the crystal quality profoundly impacts the efficiency of charge carrier transport.

Ultrafast microscopy methods traditionally assume a Gaussian profile to extract excited state diffusivities from transport measurements. Although this fitting method recovers accurate diffusion coefficients when the point spread function is well-represented by a Gaussian, even minor spatial aberrations introduced by the imaging system cause significant errors in the determined value. To provide a more accurate measure of excited state transport in nano-and microscale materials systems, an alternative analysis protocol is proposed that numerically convolves the Green's function solution to the diffusion equation with the experimentally measured point spread function. In contrast to the Gaussian fitting approach, the numerical convolution is shown to be robust against artifacts caused by nonideal point spread functions. Furthermore, the numerical convolution approach is highly effective at resolving anisotropic diffusion in modeled data.