Automated high-throughput Wannierisation
- 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
- 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
- Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, UK
- Departments of Materials and Physics, and the Thomas Young Centre for Theory and Simulation of Materials, Imperial College London, London SW7 2AZ, UK
DOI10.24435/materialscloud:2019.0044/v2 (version v2, submitted on 25 November 2019)
How to cite this entry
Jonathan R. Yates, Arash A. Mostofi, Nicola Marzari, Valerio Vitale, Giovanni Pizzi, Antimo Marrazzo, Automated high-throughput Wannierisation, Materials Cloud Archive (2019), doi: 10.24435/materialscloud:2019.0044/v2.
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.
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|4.5 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|
25 November 2019 [This version]
30 August 2019