![]() We have found that although a random alloy model captures the distribution of compositional fluctuations in relatively low In (∼18%) content InxGa1–xN QWs, there exists a striking deviation from the model in higher In content (≥24%) QWs. We have shown a direct comparison between the computationally predicted and experimentally observed compositional fluctuations. We have developed a systematic formalism based on the autonomous detection of compositional fluctuation in observed and simulated EELS maps. Employing a multiscale computational model, we show the tendency of intrinsic compositional fluctuation in InxGa1–xN QWs at different indium concentrations and in the presence of strain. Here we have combined electron energy loss spectroscopy (EELS) imaging at subnanometer resolution with multiscale computational models to obtain a statistical distribution of the compositional fluctuations in InxGa1–xN quantum wells (QWs). The accurate determination of compositional fluctuations is pivotal in understanding their role in the reduction of efficiency in high indium content InxGa1–xN light emitting diodes (LEDs), the origin of which is still poorly understood. As with any Compressed Sensing methodology, care must be taken to ensure that isolated events are not excluded from the process, but the observed increase in simulation speed provides significant opportunities for real time simulations, image classification and analytics to be performed as a supplement to experiments on a microscope to be developed in the future. Here we discuss the parameters that we use and how the resulting simulations can be quantifiably compared to the full simulations. ![]() We find that it is possible to significantly sub-sample the area that is simulated, the number of g-vectors contributing the image, and the number of frozen phonon configurations contributing to the final image while still producing an acceptable fit to a fully sampled simulation. In this paper, we apply the same inpainting approach (a form of compressed sensing) to simulated, sub-sampled atomic resolution STEM images. Recently it has been shown that precise dose control and an increase in the overall acquisition speed of atomic resolution scanning transmission electron microscope (STEM) images can be achieved by acquiring only a small fraction of the pixels in the image experimentally and then reconstructing the full image using an inpainting algorithm. The fast and accurate prediction of ADF-EDX scattering cross-sections opens up new opportunities to explore the wide range of ordering possibilities of heterogeneous materials with multiple elements. Based on incoherent imaging, the atomic lensing model previously developed for ADF is now expanded to EDX, which yields ADF-EDX scattering cross-section predictions in good agreement with multislice simulations for mixed columns in a core-shell nanoparticle and a high entropy alloy. Taking EDX as a perfect incoherent reference mode, we quantitatively examine the ADF longitudinal incoherence under different microscope conditions using multislice simulations. We first explain the origin of the ADF and EDX incoherence from scattering physics suggesting a linear dependence between those two signals in the case of a high-angle ADF detector. To overcome these challenges, we here report the development of an incoherent non-linear method for the fast prediction of ADF-EDX scattering cross-sections of mixed columns under channelling conditions. The combination of the computationally intensive nature of these simulations and the enormous amount of possible mixed column configurations for a given composition indeed severely hamper the quantification process. However, significant difficulties remain to quantify mixed columns by comparing the resulting atomic resolution images and spectroscopy data with multislice simulations where dynamic scattering needs to be taken into account. Quantitative scanning transmission electron microscopy is useful to determine the composition and thickness of nanostructures at the atomic scale. Advanced materials often consist of multiple elements which are arranged in a complicated structure.
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