Towards high-throughput many-body perturbation theory: efficient algorithms and automated workflows

DOI

The automation of ab initio simulations is essential in view of performing high-throughput (HT) computational screenings oriented to the discovery of novel materials with desired physical properties. In this work, we propose algorithms and implementations that are relevant to extend this approach beyond density functional theory (DFT), in order to automate many-body perturbation theory (MBPT) calculations. Notably, a novel algorithm pursuing the goal of an efficient and robust convergence procedure for GW and BSE simulations is provided, together with its implementation in a fully automated framework. This is accompanied by an automatic GW band interpolation scheme based on maximally-localized Wannier functions, aiming at a reduction of the computational burden of quasiparticle band structures while preserving high accuracy. The proposed developments are validated on a set of representative semiconductor and metallic systems.

Identifier
DOI https://doi.org/10.24435/materialscloud:6w-qh
Related Identifier https://doi.org/10.1038/s41524-023-01027-2
Related Identifier https://www.nature.com/articles/s41524-023-01027-2#citeas
Related Identifier https://renkulab.io/projects/new?data=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
Related Identifier https://archive.materialscloud.org/communities/mcarchive
Related Identifier https://doi.org/10.24435/materialscloud:zc-8p
Metadata Access https://archive.materialscloud.org/oai2d?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:1559
Provenance
Creator Bonacci, Miki; Qiao, Junfeng; Spallanzani, Nicola; Marrazzo, Antimo; Pizzi, Giovanni; Molinari, Elisa; Varsano, Daniele; Ferretti, Andrea; Prezzi, Deborah
Publisher Materials Cloud
Contributor Bonacci, Miki
Publication Year 2022
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type info:eu-repo/semantics/other
Format application/octet-stream; application/gzip; application/json; text/plain; text/markdown
Discipline Materials Science and Engineering