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.

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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
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article

Obtaining highly transferable parameters for empirical tight-binding simulations: From pure materials to polytypic heterostructures and dilute alloys

Matthias Auf der Maur, Alessia Di Vito, Anh‐Luan Phan, Daniele Soccodato et al.
Physical review. B./Physical review. B
Machine Learning in Materials Science
article

Obtaining highly transferable parameters for empirical tight-binding simulations: From pure materials to polytypic heterostructures and dilute alloys

Matthias Auf der Maur, Alessia Di Vito, Anh‐Luan Phan, Daniele Soccodato, Alessandro Pecchia
article en

Abstract

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.

Physical review. B./Physical review. BVol. 112(16)
University of Rome Tor Vergata (IT), Institute of Nanostructured Materials (IT), Institute of Structure of Matter (IT)
Openalex Percentile: Top 77%
Machine Learning in Materials Science
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