This record has versions v1, v2, v3. This is version v1.

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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:2019.0044/v1 [version v1]

Publication date: Aug 30, 2019

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 2019.0044/v1 (2019), doi: 10.24435/materialscloud:2019.0044/v1.


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- thoughput (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. Finally, we provide here a downloadable virtual machine that allows to reproduce the results of this paper, including all first-principles and atomistic simulations as well as the computational workflows.

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File name Size Description
1.4 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)
4.6 KiB README.txt on how to use the Virtual Machine (based on a modified version of the Quantum Mobile)
3.2 GiB VirtualBox image 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


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.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.


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