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| Hauptseite > Publikationsdatenbank > Prediction of the magnetic exchange interaction in doped topological insulators |
| Hauptseite > Publikationsdatenbank > Prediction of the magnetic exchange interaction in doped topological insulators |
| Conference Presentation (Invited) | FZJ-2024-05827 |
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2024
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Please use a persistent id in citations: doi:10.34734/FZJ-2024-05827
Abstract: We present a benchmark study of surrogate models for impurities embedded into crystalline solids. Using the Korringa-Kohn-Rostoker Green Function method and the AiiDA workflow engine [1], we have built a database of magnetic transition metal impurity dimers embedded in the topological insulator Bi2Te3. We predict isotropic exchange interaction of the impurity dimer in the classical Heisenberg model with machine learning and then use these surrogates as input for spin dynamics calculations to find the magnetic ground state of the material [2]. The study compares various recent E(3)-equivariant models such as ACE and MACE [3] in terms of performance and reproducible end-to-end workflows.References.[1] P. Rüßmann, F. Bertoldo, S. Blügel, npj. Comput. Mater., 7, 13 (2021)[2] P. Rüßmann, J. Ribas Sobreviela, M. Sallermann, M. Hoffmann, F. Rhiem, S. Blügel, Front. Mater., 9, (2022)[3] Batatia, I., Kovács, D. P., Simm, G. N. C., Ortner, C. & Csányi, G. MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields. Preprint (2022).
Keyword(s): Magnetic Materials (1st) ; Magnetism (2nd)
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