Junfeng Qiao^1*, Giovanni Pizzi^1,2*, Nicola Marzari^1,2*

1 Theory and Simulations of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

2 Laboratory for Materials Simulations (LMS), Paul Scherrer Institut (PSI), CH-5232 Villigen PSI, Switzerland

* Corresponding authors emails: junfeng.qiao@epfl.ch, giovanni.pizzi@psi.ch, nicola.marzari@epfl.ch

DOI10.24435/materialscloud:v4-e9 [version v1]

Publication date: Mar 17, 2023

How to cite this record

Junfeng Qiao, Giovanni Pizzi, Nicola Marzari, Projectability disentanglement for accurate and automated electronic-structure Hamiltonians, Materials Cloud Archive 2023.44 (2023), https://doi.org/10.24435/materialscloud:v4-e9

Description

Maximally-localized Wannier functions (MLWFs) are a powerful and broadly used tool to characterize the electronic structure of materials, from chemical bonding to dielectric response to topological properties. Most generally, one can construct MLWFs that describe isolated band manifolds, e.g. for the valence bands of insulators, or entangled band manifolds, e.g. in metals or describing both the valence and the conduction manifolds in insulators. Obtaining MLWFs that describe a target manifold accurately and with the most compact representation often requires chemical intuition and trial and error, a challenging step even for experienced researchers and a roadblock for automated high-throughput calculations. Here, we present a very natural and powerful approach that provides automatically MLWFs spanning the occupied bands and their natural complement for the empty states, resulting in WÎannier Hamiltonian models that provide a tight-binding picture of optimized atomic orbitals in crystals. Key to the success of the algorithm is the introduction of a projectability measure for each Bloch state onto atomic orbitals (here, chosen from the pseudopotential projectors) that determines if that state should be kept identically, discarded, or mixed into a disentangling algorithm. We showcase the accuracy of our method by comparing a reference test set of 200 materials against the selected-columns-of-the-density-matrix (SCDM) algorithm, and its reliability by Wannierizing 21 737 materials from the Materials Cloud.

Materials Cloud sections using this data

Files

File name	Size	Description
README.md MD5md5:abfa2619131553c67fcc3912ab891af9	5.7 KiB	Descriptions of files
list_of_calculations_for_200_structures.txt MD5md5:6b145f161fa17e5387326d89e8a68047	43.4 KiB	List of AiiDA calculations for 200 structures
list_of_calculations_for_test_structures.txt MD5md5:ff3725d27e70965f83943f2fa5c5354b	6.4 KiB	List of AiiDA calculations for 4 testing materials
PDWF_export_20230314.aiida MD5md5:2a5004c23ac7c9eb9ace5928da8ea289 Open this AiiDA archive on renkulab.io (https://renkulab.io/)	15.2 GiB	AiiDA database containing all the calculations

License

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External references

Keywords

Wannier functions high-throughput Wannierization MARVEL SNSF