Linear-scaling algorithm for rapid computation of inelastic transitions in the presence of multiple electron scattering

Strong multiple scattering of the probe in scanning transmission electron microscopy (STEM) means image simulations are usually required for quantitative interpretation and analysis of elemental maps produced by electron energy-loss spectroscopy (EELS). These simulations require a full quantum-mechanical treatment of multiple scattering of the electron beam, both before and after a core-level inelastic transition. Current algorithms scale quadratically and can take up to a week to calculate on desktop machines even for simple crystal unit cells and do not scale well to the nanoscale heterogeneous systems that are often of interest to materials science researchers. We introduce an algorithm with linear scaling that typically results in an order of magnitude reduction in computation time for these calculations without introducing additional error and discuss approximations that further improve computational scaling for larger-scale objects with modest penalties in calculation error. We demonstrate these speedups by calculating the atomic resolution STEM-EELS map using the L-edge transition of Fe, for a nanoparticle 80 Å in diameter, in 16 hours, a calculation that would have taken at least 80 days using a conventional multislice approach

Structure retrieval at atomic resolution in the presence of multiple scattering of the electron probe

The projected electrostatic potential of a thick crystal is reconstructed at atomic-resolution from experimental scanning transmission electron microscopy data recorded using a new generation fast- readout electron camera. This practical and deterministic inversion of the equations encapsulating multiple scattering that were written down by Bethe in 1928 removes the restriction of established methods to ultrathin ($\lesssim 50$ {\AA}) samples. Instruments already coming on-line can overcome the remaining resolution-limiting effects in this method due to finite probe-forming aperture size, spatial incoherence and residual lens aberrations.Comment: 6 pages, 3 figure

Limited metacognitive access to one’s own facial expressions

As humans we communicate important information through fine nuances in our facial expressions, but because conscious motor representations are noisy, we might not be able to report these fine but meaningful movements. Here we measured how much explicit metacognitive information young adults have about their own facial expressions. Participants imitated pictures of themselves making facial expressions and triggered a camera to take a picture of them while doing so. They then rated confidence (how well they thought they imitated each expression). We defined metacognitive access to facial expressions as the relationship between objective performance (how well the two pictures matched) and subjective confidence ratings. Metacognitive access to facial expressions was very poor when we considered all face features indiscriminately. Instead, machine learning analyses revealed that participants rated confidence based on idiosyncratic subsets of features. We conclude that metacognitive access to own facial expressions is partial, and surprisingly limited