Berlin 2024 – scientific programme
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MM: Fachverband Metall- und Materialphysik
MM 43: Data Driven Material Science: Big Data and Workflows V
MM 43.5: Talk
Wednesday, March 20, 2024, 16:45–17:00, C 243
Automated prediction of Fermi surfaces from first principles — •Nataliya Paulish1, Junfeng Qiao2, and Giovanni Pizzi1,2 — 1Paul Scherrer Institut (PSI), Villigen, Switzerland — 2École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Knowing the shape of the Fermi surface (FS) and the energy dispersion in its vicinity is crucial to understand the electronic properties of materials and identify materials relevant for applications. Experimental methods to measure FSs are very expensive and time-consuming, and accurate theoretical predictions would help to get deeper insights from the experimental data. Direct first-principles calculations of the FS, requiring very dense sampling in reciprocal space, are thus limited by the computational cost. To accelerate the calculations, we use interpolation with Maximally Localized Wannier Functions (MLWFs), powered by our new method that allows fully automated calculation of MLWFs [1]. We first validate the numerical approach by comparing our simulation results with literature data for de Haas-van Alphen (dHvA) oscillation frequencies and investigate the main sources of numerical errors. We then use our high-throughput setup, with our code implemented as an AiiDA [2] workflow, to create a large database of Fermi surfaces and dHvA oscillation frequencies of 3D inorganic metals, starting from high-symmetry systems.
[1] J. Qiao et al., npj Comput. Mater. 9, 208 (2023)
[2] S. P. Huber et al., Scientific Data, 7, 300 (2020)
Keywords: Fermi surfaces; Wannier functions; de Haas-van Alphen effect; AiiDA workflows; automated wannierisation