English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Quantum-mechanical (QM) simulations, thanks to their predictive power, canprovide significant insights into the nature and dynamics of defects such asvacancies, dislocations and grain boundaries. These considerations areessential in the context of the development of reliable, inexpensive andenvironmentally friendly alloys. However, despite significant progress incomputer performance, QM simulations of defects are still extremelytime-consuming with ab-initio/non-parametric methods. The two-centreSlater-Koster (SK) tight-binding (TB) models can achieve significantcomputational efficiency and provide an interpretable picture of the electronicstructure. In some cases, this makes TB a compelling alternative to modelsbased on abstraction of the electronic structure, such as the embedded atommodel. The biggest challenge in the implementation of the SK method is theestimation of the optimal and transferable parameters that are used toconstruct the Hamiltonian matrix. In this paper, we will present results of thedevelopment of a data-driven framework, following the classical approach ofadjusting parameters in order to recreate properties that can be measured orestimated using ab-initio or non-parametric methods. Distinct features includeincorporation of data from QSGW (quasi-particle self-consistent GWapproximation) calculations, as well as consideration of higher-order elasticconstants. Furthermore, we provide a description of the optimisation procedure,omitted in many publications, including the design stage. We also apply modernoptimisation techniques that allow us to minimise constraints on the parameterspace. In summary, this paper introduces some methodological improvements tothe semi-empirical approach while addressing associated challenges andadvantages.

modelling and simulation in materials science and engineering

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