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| Author |
Annys, A.; Jannis, D.; Verbeeck, J. |
| Title |
Core-loss EELS dataset and neural networks for element identification |
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Dataset |
| Year |
2023 |
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Dataset; Electron microscopy for materials research (EMAT) |
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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) |
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UA @ admin @ c:irua:203391 |
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9015 |
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Zhang, Z.; Lobato, I.; Brown, H.; Jannis, D.; Verbeeck, J.; Van Aert, S.; Nellist, P. |
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Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions |
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2023 |
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Dataset; Electron microscopy for materials research (EMAT) |
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Inelastic excitation as exploited in Electron Energy Loss Spectroscopy (EELS) contains a rich source of information that is revealed in the scattering process. To accurately quantify core-loss EELS, it is common practice to fit the observed spectrum with scattering cross-sections calculated using experimental parameters and a Generalized Oscillator Strength (GOS) database [1]. The GOS is computed using Fermi’s Golden Rule and orbitals of bound and excited states. Previously, the GOS was based on Hartree-Fock solutions [2], but more recently Density Functional Theory (DFT) has been used [3]. In this work, we have chosen to use the Dirac equation to incorporate relativistic effects and have performed calculations using Flexible Atomic Code (FAC) [4]. This repository contains a tabulated GOS database based on Dirac solutions for computing double differential cross-sections under experimental conditions. We hope the Dirac-based GOS database can benefit the EELS community for both academic use and industry integration. Database Details: – Covers all elements (Z: 1-108) and all edges – Large energy range: 0.01 – 4000 eV – Large momentum range: 0.05 -50 Å-1 – Fine log sampling: 128 points for energy and 256 points for momentum – Data format: GOSH [3] Calculation Details: – Single atoms only; solid-state effects are not considered – Unoccupied states before continuum states of ionization are not considered; no fine structure – Plane Wave Born Approximation – Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when – core-shell electron is excited – Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; Local Density Approximation is assumed for electron exchange interactions; continuum states are normalized against asymptotic form at large distances – Both large and small component contributions of Dirac solutions are included in GOS – Final state contributions are included until the contribution of the previous three states falls below 0.1%. A convergence log is provided for reference. Version 1.1 release note: – Update to be consistent with GOSH data format [3], all the edges are now within a single hdf5 file. A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å. Great thanks to Gulio Guzzinati for his suggestions and sending conversion script. Version 1.2 release note: – Add “File Type / File version” information [1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224. [2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243. [3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765 [4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689. |
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UA @ admin @ c:irua:203392 |
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9042 |
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Grünewald, L.; Chezganov, D.; De Meyer, R.; Orekhov, A.; Van Aert, S.; Bogaerts, A.; Bals, S.; Verbeeck, J. |
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Supplementary Information for “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” |
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2023 |
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Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
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Supplementary information for the article “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” containing the videos of in-situ SEM imaging (mp4 files), raw data/images, and Jupyter notebooks (ipynb files) for data treatment and plots. Link to the preprint: https://doi.org/10.48550/arXiv.2308.15123 Explanation of the data files can be found in the Information.pdf file. The Videos folder contains the in-situ SEM image series mentioned in the paper. If there are any questions/bugs, feel free to contact me at lukas.grunewaldatuantwerpen.be |
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UA @ admin @ c:irua:203389 |
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9100 |
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Brognara, A.; Kashiwar, A.; Jung, C.; Zhang, X.; Ahmadian, A.; Gauquelin, N.; Verbeeck, J.; Djemia, P.; Faurie, D.; Dehm, G.; Idrissi, H.; Best, J.P.; Ghidelli, M. |
| Title |
Tailoring mechanical properties and shear band propagation in ZrCu metallic glass nanolaminates through chemical heterogeneities and interface density |
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A1 Journal article |
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2024 |
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Small Structures |
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2400011-11 |
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A1 Journal article; Electron microscopy for materials research (EMAT) |
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The design of high‐performance structural thin films consistently seeks to achieve a delicate equilibrium by balancing outstanding mechanical properties like yield strength, ductility, and substrate adhesion, which are often mutually exclusive. Metallic glasses (MGs) with their amorphous structure have superior strength, but usually poor ductility with catastrophic failure induced by shear bands (SBs) formation. Herein, we introduce an innovative approach by synthesizing MGs characterized by large and tunable mechanical properties, pioneering a nanoengineering design based on the control of nanoscale chemical/structural heterogeneities. This is realized through a simplified model Zr 24 Cu 76 /Zr 61 Cu 39 , fully amorphous nanocomposite with controlled nanoscale periodicity ( Λ , from 400 down to 5 nm), local chemistry, and glass–glass interfaces, while focusing in‐depth on the SB nucleation/propagation processes. The nanolaminates enable a fine control of the mechanical properties, and an onset of crack formation/percolation (>1.9 and 3.3%, respectively) far above the monolithic counterparts. Moreover, we show that SB propagation induces large chemical intermixing, enabling a brittle‐to‐ductile transition when Λ ≤ 50 nm, reaching remarkably large plastic deformation of 16% in compression and yield strength ≈2 GPa. Overall, the nanoengineered control of local heterogeneities leads to ultimate and tunable mechanical properties opening up a new approach for strong and ductile materials. |
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2024-05-20 |
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2688-4062 |
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Most recent IF: NA |
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UA @ admin @ c:irua:205798 |
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9176 |
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