Hauptseite > Publikationsdatenbank > Magnetic Multilayers: From High-Throughput Ab-initio Calculations to Predictive Machine Learning > print

001     1016528
005     20231102201846.0
024 7 _ |a 10.34734/FZJ-2023-03694
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037 _ _ |a FZJ-2023-03694
041 _ _ |a English
100 1 _ |a Hilgers, Robin
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111 2 _ |a CMD30 FisMat2023
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|d 2023-09-04 - 2023-09-08
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245 _ _ |a Magnetic Multilayers: From High-Throughput Ab-initio Calculations to Predictive Machine Learning
260 _ _ |c 2023
336 7 _ |a Conference Paper
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336 7 _ |a Conference Presentation
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520 _ _ |a Thin-film multi-layer systems of 3d transition metals exhibiting magnetic phenomena are prototype systems in the field of surface magnetism [1,2]. The possibility to tune the interactions by choosing different elements and different stacking, enables scientists and engineers to design materials with specific desired magnetic properties. Up to now, the magnetic properties are rarely examined using high-throughput simulations due to the peculiarities of the setup and technical challenges induced by the surface setup in DFT [3] calculations.We performed a combinatorics study of such multilayer surface systems by employing a layer swapping based approach using three different mono-atomic 3d transition metal layers on noble metal (FCC) substrates. Hence, we systematically constructed symmetric thin-films and computed the resulting magnetic properties. By using a highly automated AiiDA [4,5] workflow and our all-electron full-potential DFT code FLEUR [3] we studied 6660 possible configurations of film systems. This systematic approach enables us to perform a detailed analysis of the underlying physics, magnetic properties as well as to apply ML and XAI techniques on the acquired data. Concluding, we demonstrate the capabilities of state-of-the-art computational frameworks [4,5,6] and workflows [7] in high-throughput materials screening.Acknowledgement: This work was performed as part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) and received funding from the Helmholtz Association of German Research Centres. References:[1] Zhang, L. (2022). Topological magnonic properties of two-dimensional magnetic materials (Vol. 253, p. 154 p.) [Dissertation, Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag]. https://juser.fz-juelich.de/record/907375[2] Blügel S. Two-dimensional ferromagnetism of 3d, 4d, and 5d transition metal monolayers on noble metal (001) substrates. Phys Rev Lett. 1992 Feb 10;68(6):851-854. doi: 10.1103/PhysRevLett.68.851. PMID: 10046009.[3] FLEUR Code www.flapw.de[4] S. P. Huber et al., AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance, Scientific Data 7, 300 (2020); DOI: 10.1038/s41597-020-00638-4[5] Jens Bröder, Vasily Tseplyaev, Henning Janssen, Anoop Chandran, Daniel Wortmann, & Stefan Blügel. (2022). JuDFTteam/aiida-fleur: AiiDA-FLEUR (v.1.3.1). Zenodo. https://doi.org/10.5281/zenodo.6420726[6] Bröder, J. (2021). High-throughput All-Electron Density Functional Theory Simulations for a Data-driven Chemical Interpretation of X-ray Photoelectron Spectra (Vol. 229, pp. viii, 169, XL S.) [Dissertation, Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag]. https://juser.fz-juelich.de/record/891865[7] Vasily Tseplyaev, PhD Thesis. Unpublished.
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536 _ _ |a HDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)
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650 2 7 |a Condensed Matter Physics
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650 2 7 |a Magnetism
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650 2 7 |a Materials Science
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650 1 7 |a Magnetic Materials
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700 1 _ |a Wortmann, Daniel
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700 1 _ |a Blügel, Stefan
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856 4 _ |u https://eventi.cnism.it/cmd30-fismat/submission/view/13645588601744479196
856 4 _ |u https://juser.fz-juelich.de/record/1016528/files/Presentation.pdf
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