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001031807 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-05827
001031807 037__ $$aFZJ-2024-05827
001031807 041__ $$aEnglish
001031807 1001_ $$0P:(DE-Juel1)186072$$aWasmer, Johannes$$b0$$eCorresponding author$$ufzj
001031807 1112_ $$aMachine Learning of First Principles Observables$$cBerlin$$d2024-07-08 - 2024-07-12$$gmlfpo24$$wGermany
001031807 245__ $$aPrediction of the magnetic exchange interaction in doped topological insulators
001031807 260__ $$c2024
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001031807 502__ $$cRWTH Aachen
001031807 520__ $$aWe 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).
001031807 536__ $$0G:(DE-HGF)POF4-5211$$a5211 - Topological Matter (POF4-521)$$cPOF4-521$$fPOF IV$$x0
001031807 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x1
001031807 536__ $$0G:(DE-Juel-1)aidas_20200731$$aAIDAS - Joint Virtual Laboratory for AI, Data Analytics and Scalable Simulation (aidas_20200731)$$caidas_20200731$$x2
001031807 536__ $$0G:(DE-Juel1)HDS-LEE-20190612$$aHDS LEE - Helmholtz School for Data Science in Life, Earth and Energy (HDS LEE) (HDS-LEE-20190612)$$cHDS-LEE-20190612$$x3
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001031807 65027 $$0V:(DE-MLZ)SciArea-170$$2V:(DE-HGF)$$aMagnetism$$x0
001031807 65017 $$0V:(DE-MLZ)GC-1604-2016$$2V:(DE-HGF)$$aMagnetic Materials$$x0
001031807 7001_ $$0P:(DE-Juel1)186673$$aAntognini Silva, David$$b1$$eContributor$$ufzj
001031807 7001_ $$0P:(DE-Juel1)185917$$aMozumder, Rubel$$b2
001031807 7001_ $$0P:(DE-Juel1)157882$$aRüssmann, Philipp$$b3$$eContributor$$ufzj
001031807 7001_ $$0P:(DE-Juel1)130548$$aBlügel, Stefan$$b4$$eContributor$$ufzj
001031807 8564_ $$uhttps://iffgit.fz-juelich.de/phd-project-wasmer/presentations/2024-07-08-talk-mlfpo24
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001031807 9141_ $$y2024
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