Benchmark study of symmetry-adapted ML-DFT models for magnetically doped topological insulators
Benchmark study of symmetry-adapted ML-DFT models for magnetically doped topological insulators
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 of the topological insulator Bi2Te3, magnetically co-doped with transition metal impurities (dimers). 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 NequIP [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:2205.06643 (2022)
Psi-k 2022 Conference, psik2022, Lausanne, RWTH Aachen University, Switzerland, 22 Aug 2022 - 25 Aug 2022
- Humboldt-Universität zu Berlin Germany
- Freie Universität Berlin Germany
- Forschungszentrum Jülich Germany
- Helmholtz Association of German Research Centres Germany
- University of Würzburg Germany
property prediction, KKR method, magnetic interaction, quantum materials, AiiDA, materials database, scientific workflows, graph neural networks, JuDFT, JuKKR, Heisenberg model, AiiDA-KKR, material defects, database generation, topological insulators, machine learning, magnetism, active learning, impurity embedding, density functional theory, atomistic machine learning
property prediction, KKR method, magnetic interaction, quantum materials, AiiDA, materials database, scientific workflows, graph neural networks, JuDFT, JuKKR, Heisenberg model, AiiDA-KKR, material defects, database generation, topological insulators, machine learning, magnetism, active learning, impurity embedding, density functional theory, atomistic machine learning
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