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| Home > Publications database > High-throughput All-Electron Density Functional Theory Simulations for a Data-driven Chemical Interpretation of X-ray Photoelectron Spectra |
| Home > Publications database > High-throughput All-Electron Density Functional Theory Simulations for a Data-driven Chemical Interpretation of X-ray Photoelectron Spectra |
| Book/Dissertation / PhD Thesis | FZJ-2021-01785 |
2021
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
Jülich
ISBN: 978-3-95806-526-0
Please use a persistent id in citations: http://hdl.handle.net/2128/27638 urn:nbn:de:0001-2021052737
Abstract: Enabling computer-driven materials design to find and create materials with advanced propertiesfromthe enormous haystack of material phase space is a worthy goal for humanity. Most high-technologies, for example in the energy or health sector, strongly depend on advanced tailored materials. Since conventional research and screening of materials is rather slow and expensive, being able to determine material properties on the computer poses a paradigm shift. For the calculation of properties for pure materials on the nano scale ab initio methods based on the theory of quantum mechanics are well established. Density Functional Theory(DFT) is such a widely applied method from first principles with high predictive power. To screen through larger sets of atomic configurations physical property calculation processes need to be robust and automated. Automation is achieved through the deployment of advanced frameworks which manage many workflows while tracking the provenance of data and calculations. Through workflows, which are essential property calculator procedures, a high-level automation environment is achievable and accumulated knowledge can be reused by others. Workflows can be complex and include multiple programs solving problems over several physical length scales. In this work, the open source all-electron DFT program FLEUR implementing the highly accurate Full-potential Linearized Augmented Plane Wave (FLAPW) method is connected and deployed through the open source Automated Interactive Infrastructure and Database for Computational Science (AiiDA) framework to achieve automation. AiiDA is a Python framework which is capable of provenance tracking millions of high-through put simulations and their data. Basic and advanced workflows are implemented in an open source Pythonpackage AiiDA-FLEUR, especially to calculate properties for the chemical analysis of X-rayphotoemission spectra. These workflows are applied on a wide range of materials, in particular on most known metallic binary compounds. The chemical-phase composition and other material properties of a surface region can be understood through the careful chemical analysis of high-resolution X-ray photoemission spectra. The spectra evaluation process is improved through the development of a fittingmethod driven by data from ab initio simulations. For complex multi-phase spectra this proposedevaluation process is expected to have advantages over the widely applied conventional methods. The spectra evaluation process is successfully deployed on well-behaved spectra of materials relevant for the inner wall (blanket and divertor) plasma-facing components of a nuclear fusion reactor. In particular, the binary beryllium systems Be-Ti, Be-Wand Be-Ta are investigated. Furthermore, different approaches to calculate spectral properties like chemical shifts and binding energies are studied and benchmarked against the experimental literature and data from the NIST X-ray photoelectron spectroscopy database.
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