{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/py-gwbse-a-high-throughput-workflow-package","title":"$py$GWBSE: A high throughput workflow package for GW-BSE calculations","arxiv_id":"2210.00152","date":"2022-10-01","proceeding":null,"authors":["Tathagata Biswas","Arunima K. Singh"],"abstract":"We develop an open-source python workflow package, $py$GWBSE to perform automated first-principles calculations within the GW-BSE (Bethe-Salpeter) framework. GW-BSE is a many body perturbation theory based approach to explore the quasiparticle (QP) and excitonic properties of materials. The GW approximation has proven to be effective in accurately predicting bandgaps of a wide range of materials by overcoming the bandgap underestimation issues of the more widely used density functional theory (DFT). The BSE formalism, in spite of being computationally expensive, produces absorption spectra directly comparable with experimental observations. The $py$GWBSE package achieves complete automation of the entire multi-step GW-BSE computation, including the convergence tests of several parameters that are crucial for the accuracy of these calculations. $py$GWBSE is integrated with $Wannier90$, a program for calculating maximally-localized wannier functions, allowing the generation of QP bandstructures. $py$GWBSE also enables automated creation of databases of metadata and data, including QP and excitonic properties, which can be extremely useful for future material discovery studies in the field of ultra-wide bandgap semiconductors, electronics, photovoltaics, and photocatalysis.","url_abs":"https://arxiv.org/abs/2210.00152v2","url_pdf":"https://arxiv.org/pdf/2210.00152v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"py-gwbse-a-high-throughput-workflow-package","repo_url":"https://github.com/cmdlab/pygwbse","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}