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| Hauptseite > Publikationsdatenbank > Benchmark study of symmetry-adapted ML-DFT models for magnetically doped topological insulators |
| Hauptseite > Publikationsdatenbank > Benchmark study of symmetry-adapted ML-DFT models for magnetically doped topological insulators |
| Conference Presentation (Invited) | FZJ-2023-05860 |
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2023
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Please use a persistent id in citations: doi:10.34734/FZJ-2023-05860
Abstract: We present a benchmark study of surrogate models for impurities embedded into crystalline solids. Using the Korringa-Kohn-Rostoker Green Function method [1], we have built databases of several thousand calculations of single impurities (monomers) embedded into different elemental crystals, as well as magnetic transition metal impurity dimers embedded in the topological insulator Bi2Te3. We predict the converged monomer impurity electron potential and the isotropic exchange interaction of the impurity dimer in the classical Heisenberg model. From these surrogates, we intend to build transferable models for larger systems in the future, which will accelerate the convergence of our DFT codes. The study compares various recent E(3)-equivariant models such as ACE and MACE [2] in terms of performance and reproducible end-to-end workflows. [1] P. Rüßmann et al., npj Comput Mater 7, 13 (2021) [2] I. Batatia et al., arXiv:2206.07697 (2022)
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