Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.

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: This chapter lays out a conservation timeline, from past to future, for the Amsterdam version of Van Gogh's Sunflowers. It starts by considering the restoration history of the painting in order to assess its current physical state, and looks ahead to formulate an appropriate strategy for future conservation treatment and display. Due attention is paid to the two recorded episodes of restoration performed in 1927 and 1961 by the Dutch restorer, Jan Cornelis Traas. Based on physical and chemical investigation of Sunflowers we attempt to reconstruct what these former treatments (which are barely documented) entailed and consider the repercussions for the present condition of the painting. The former interventions by Traas also serve as a benchmark to reflect on current choices made, highlighting the extent to which ideas and methodologies have continued to evolve over the past century as conservation has moved further away from being a singularly craft-based activity to become an established historical and scientific discipline underpinned by ethical guidelines. Jan Cornelis Traas (1898–1984) As mentioned, the two main recorded interventions to the Amsterdam Sunflowers may be associated with the Dutch restorer, Jan Cornelis Traas, who treated the picture in 1927, close to the start of his career, and again in 1961, shortly before he retired. Traas was the first restorer to be appointed at the Mauritshuis in The Hague where he worked from 1931 to 1962 and treated hundreds of paintings, including iconic masterpieces such as Girl with a Pearl Earring by Johannes Vermeer. Yet despite the magnitude and importance of his restoration oeuvre, J.C. Traas (as he is usually referred to in surviving documents), has remained somewhat obscure. He is shown here in the only known surviving photograph of him at work, shortly before he retired (fig. 7.1). Unlike his illustrious contemporaries, A. Martin de Wild (1899–1969) and Helmut Ruhemann (1891–1973), for example, Traas did not publish anything, he appears to have kept no records of his work and no personal archive is known. However, the study of some newly discovered historical documents, combined with physical examination of Sunflowers and a large number of other works he treated, allows us to recover an idea of his working practices and approaches viewed within the context of his day.