  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.
