Abstract: We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification.  The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra. Code on how to  – read the simulated data – transform HDF5 format to TFRecord format – train and evaluate neural networks using the simulated data – use the trained networks for automated element identification is available on GitHub at arnoannys/EELS_ID A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at:                    Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. Sci Rep 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7 [1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585 [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323)

Abstract: Recent advances in the development of electron and X-ray detectors have opened up the possibility to detect single events from which its time of arrival can be determined with nanosecond resolution. This allows observing time correlations between electrons and X-rays in the transmission electron microscope. In this work, a novel setup is described which measures individual events using a silicon drift detector and digital pulse processor for the X-rays and a Timepix3 detector for the electrons. This setup enables recording time correlation between both event streams while at the same time preserving the complete conventional electron energy loss (EELS) and energy dispersive X-ray (EDX) signal. We show that the added coincidence information improves the sensitivity for detecting trace elements in a matrix as compared to conventional EELS and EDX. Furthermore, the method allows the determination of the collection efficiencies without the use of a reference sample and can subtract the background signal for EELS and EDX without any prior knowledge of the background shape and without pre-edge fitting region. We discuss limitations in time resolution arising due to specificities of the silicon drift detector and discuss ways to further improve this aspect.

Abstract: Electrostriction is a property of dielectric materials whereby an applied electric field induces a mechanical deformation proportional to the square of that field. The magnitude of the effect is usually minuscule (<10(-19) m(2) V-2 for simple oxides). However, symmetry-breaking phenomena at the interfaces can offer an efficient strategy for the design of new properties(1,2). Here we report an engineered electrostrictive effect via the epitaxial deposition of alternating layers of Gd2O3-doped CeO2 and Er2O3-stabilized delta-Bi2O3 with atomically controlled interfaces on NdGaO3 substrates. The value of the electrostriction coefficient achieved is 2.38 x 10(-14) m(2) V-2, exceeding the best known relaxor ferroelectrics by three orders of magnitude. Our theoretical calculations indicate that this greatly enhanced electrostriction arises from coherent strain imparted by interfacial lattice discontinuity. These artificial heterostructures open a new avenue for the design and manipulation of electrostrictive materials and devices for nano/micro actuation and cutting-edge sensors.