Valerio Vitale¹, Giovanni Pizzi^2*, 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: giovanni.pizzi@epfl.ch

DOI10.24435/materialscloud:2019.0044/v2 [version v2]

Publication date: Nov 25, 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/v2 (2019), https://doi.org/10.24435/materialscloud:2019.0044/v2

Description

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. 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

Files

File name	Size	Description
README.txt MD5md5:5cef5464c4f9247d1fd8cedf56aa81a1	4.5 KiB	General information and instructions about this entry
xsf_insulators.tar.gz MD5md5:ca59da4cbc07001d89aed01b20d9c097	17.7 KiB	Crystal structures (XSF format) of the 81 insulating systems used in the paper when considering the valence bands only
xsf.tar.gz MD5md5:234323af93a2479729ba9b9f233930f7	30.1 KiB	Crystal structures (XSF format) of the 200 systems used in the paper (insulators including conduction bands, or metals)
automated_wannier_discover_data.json MD5md5:61c4b8f06e62cfd8771d201fb7defcaf	1.2 MiB	Data on spreads and bands distance of all simulations
README-virtual-machine.txt MD5md5:9e3898b17a6057f4920113efbab2dd82	2.0 KiB	README.txt on how to install the Virtual Machine
tutorial-with-screenshots-VM.pdf MD5md5:066107ad91004f19bb636d2766b9e8e6	2.4 MiB	Instructions with screenshots on how to run a Wannierisation in the virtual machine that we provide
wannierising_machine_19.07.ova MD5md5:959502fc0b894c5532d45594e6f9656f	3.2 GiB	VirtualBox image (based on a modified version of the Quantum Mobile) to install the virtual machine to run automatic Wannierisation
wannierising_machine_19.07_ansible_scripts.tar.gz MD5md5:9007aa73602d2539af4316cf77b77f05	268.3 MiB	Ansible scripts to regenerate the virtual machine from scratch
LICENSE.txt MD5md5:809241d8b968e749d93f95131f697efd	649 Bytes	License information
README-AiiDA.txt MD5md5:e3cdd8618d438e2239763c6d2efc4d6f	5.4 KiB	README.txt on how to import the AiiDA provenance database and access the data
automatic_wannier_provenance.aiida MD5md5:a06074da73513af368ecd3293671aa2f Open this AiiDA archive on renkulab.io (https://renkulab.io/)	23.3 GiB	AiiDA database, ready to be imported, with the provenance of all calculations run in the project

License

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.

External references

Keywords

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