Automated high-throughput Wannierisation

Valerio Vitale1, Giovanni Pizzi2*, Antimo Marrazzo2, Jonathan R. Yates3, Nicola Marzari2, Arash A. Mostofi4

1 Cavendish Laboratory, Department of Physics, University of Cambridge, 19 JJ Thomson Avenue Cambridge UK and Departments of Materials and Physics, and the Thomas Young Centre for Theory and Simulation of Materials, Imperial College London, London SW7 2AZ, UK

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

3 Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK

4 Departments of Materials and Physics, and the Thomas Young Centre for Theory and Simulation of Materials, Imperial College London, London SW7 2AZ, UK

* Corresponding authors emails:
DOI10.24435/materialscloud:dd-nz [version v3]

Publication date: Jun 21, 2020

How to cite this record

Valerio Vitale, Giovanni Pizzi, Antimo Marrazzo, Jonathan R. Yates, Nicola Marzari, Arash A. Mostofi, Automated high-throughput Wannierisation, Materials Cloud Archive 2020.60 (2020), doi: 10.24435/materialscloud:dd-nz.


Maximally-localised Wannier functions (MLWFs) are routinely used to compute from first-principles advanced materials properties that require very dense Brillouin zone integration and to build accurate tight-binding models for scale-bridging simulations. At the same time, high-throughput (HT) computational materials design is an emergent field that promises to accelerate the reliable and cost-effective design and optimisation of new materials with target properties. The use of MLWFs in HT workflows has been hampered by the fact that generating MLWFs automatically and robustly without any user intervention and for arbitrary materials is, in general, very challenging. We address this problem directly by proposing a procedure for automatically generating MLWFs for HT frameworks. Our approach is based on the selected columns of the density matrix method (SCDM) and we present the details of its implementation in an AiiDA workflow. We apply our approach to a dataset of 200 bulk crystalline materials that span a wide structural and chemical space. We assess the quality of our MLWFs in terms of the accuracy of the band-structure interpolation that they provide as compared to the band-structure obtained via full first-principles calculations. We provide here an AiiDA export file with the full provenance of all simulations run in the project. Moreover, we provide a downloadable virtual machine that allows to reproduce the results of this paper and also to run new calculations for different materials, including all first-principles and atomistic simulations and the computational workflows.

Materials Cloud sections using this data

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File name Size Description
5.3 KiB General information and instructions about this entry
17.7 KiB Crystal structures (XSF format) of the 81 insulating systems used in the paper when considering the valence bands only
30.1 KiB Crystal structures (XSF format) of the 200 systems used in the paper (insulators including conduction bands, or metals)
1.2 MiB Data on spreads and bands distance of all simulations
2.0 KiB README.txt on how to install the Virtual Machine
2.4 MiB Instructions with screenshots on how to run a Wannierisation in the virtual machine that we provide
3.2 GiB VirtualBox image (based on a modified version of the Quantum Mobile) to install the virtual machine to run automatic Wannierisation
268.3 MiB Ansible scripts to regenerate the virtual machine from scratch
649 Bytes License information
5.4 KiB README.txt on how to import the AiiDA provenance database and access the data
23.3 GiB AiiDA database, ready to be imported, with the provenance of all calculations run in the project
162.9 MiB PDF with all band structures computed in this work for easy inspection
5.6 KiB Fermi energy of all materials as computed by Quantum ESPRESSO


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International. Note that custom open-source licenses apply to the codes distributed in the virtual machine.

External references

Journal reference (Paper where the method and algorithms are discussed and presented)
Valerio Vitale, Giovanni Pizzi, Antimo Marrazzo, Jonathan R. Yates, Nicola Marzari, Arash A. Mostofi, npj Computational Materials 6, 66 (2020) doi:10.1038/s41524-020-0312-y


MARVEL/OSP Maximally localised Wannier functions High throughput simulations Automated Wannierisation Band-structure interpolation AiiDA workflow SCDM