Automated high-throughput Wannierisation
Abstract
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. Finally, we provide a downloadable virtual machine that can be used to reproduce the results of this paper, including all first-principles and atomistic simulations as well as the computational workflows.
Keywords
Cite
@article{arxiv.1909.00433,
title = {Automated high-throughput Wannierisation},
author = {Valerio Vitale and Giovanni Pizzi and Antimo Marrazzo and Jonathan R. Yates and Nicola Marzari and Arash A. Mostofi},
journal= {arXiv preprint arXiv:1909.00433},
year = {2020}
}
Comments
In addition to the main manuscript and supplemental materials, we have added in the Materials Cloud entry a dataset with the Wannierized band structures for all 200 materials (which can be downloaded from \url{https://archive.materialscloud.org/record/file?file_id=22842ba6-5528-48d7-9005-daa8d6a32d9d&record_id=425&filename=Vitale-2020-all-bands.pdf})