Obtaining highly transferable parameters for empirical tight-binding simulations: From pure materials to polytypic heterostructures and dilute alloys
The tedious parametrization process remains a key bottleneck hindering broader adoption of the empirical tight-binding method. Here, we discuss the challenges and requirements of finding parameters for Slater-Koster-based empirical tight-binding schemes, and explain the shortcomings of the parametrization procedures commonly used in the literature. We then propose our way to address these challenges, through the presentation of an open-sourced parametrization toolbox that breaks up the task into several, incremental stages. The prototype of our toolbox is tailored especially to deal with a recently proposed, highly transferable scheme, but can be easily adapted to any Slater-Koster-based scheme. A case study of fitting GaN parameters is considered. Remarkably, the obtained GaN parameter set shows high transferability when applied to various contexts: from simple zincblende or wurtzite bulk cases, to polytypic heterostructures and even highly mismatched alloys in their dilute limits. This confirms the effectiveness and robustness of our parametrization approach.
Authors
- Matthias Auf der Maur (ORCID: https://orcid.org/0000-0002-4815-4485)
- Alessia Di Vito (ORCID: https://orcid.org/0000-0003-4774-3488)
- Anh‐Luan Phan (ORCID: https://orcid.org/0000-0003-4283-3054)
- Daniele Soccodato (ORCID: https://orcid.org/0009-0006-5673-7548)
- Alessandro Pecchia (ORCID: https://orcid.org/0000-0001-8093-8610)
Institutions
- University of Rome Tor Vergata (IT)
- Institute of Nanostructured Materials (IT)
- Institute of Structure of Matter (IT)
Publication Details
- Journal
- Physical review. B./Physical review. B
- Published
- 2025-10-16
- DOI
- https://doi.org/10.1103/g9cz-c3js
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00