Development of data-driven spd tight-binding models of Fe -- parameterisation based on QSGW and DFT calculations including information about higher-order elastic constants

TL;DR

This paper develops a data-driven, parameterized tight-binding model for iron, incorporating QSGW and DFT data, including higher-order elastic constants, to improve accuracy and transferability for defect simulations.

Contribution

It introduces a novel framework that integrates QSGW and elastic constants into tight-binding parameterization, with detailed optimization procedures for better model transferability.

Findings

Incorporation of QSGW data enhances model accuracy.

Consideration of higher-order elastic constants improves predictive capabilities.

Modern optimization techniques effectively refine model parameters.

Abstract

Quantum-mechanical (QM) simulations, thanks to their predictive power, can provide significant insights into the nature and dynamics of defects such as vacancies, dislocations and grain boundaries. These considerations are essential in the context of the development of reliable, inexpensive and environmentally friendly alloys. However, despite significant progress in computer performance, QM simulations of defects are still extremely time-consuming with ab-initio/non-parametric methods. The two-centre Slater-Koster (SK) tight-binding (TB) models can achieve significant computational efficiency and provide an interpretable picture of the electronic structure. In some cases, this makes TB a compelling alternative to models based on abstraction of the electronic structure, such as the embedded atom model. The biggest challenge in the implementation of the SK method is the estimation of the optimal and transferable parameters that are used to construct the Hamiltonian matrix. In this paper, we will present results of the development of a data-driven framework, following the classical approach of adjusting parameters in order to recreate properties that can be measured or estimated using ab-initio or non-parametric methods. Distinct features include incorporation of data from QSGW (quasi-particle self-consistent GW approximation) calculations, as well as consideration of higher-order elastic constants. Furthermore, we provide a description of the optimisation procedure, omitted in many publications, including the design stage. We also apply modern optimisation techniques that allow us to minimise constraints on the parameter space. In summary, this paper introduces some methodological improvements to the semi-empirical approach while addressing associated challenges and advantages.