In this training, we will highlight the capabilities of MedeA for performing configurational sampling of alloys and doped structures. These methods involve random substitutions, special quasi-random structures, cluster expansion methods, molecular dynamics of doped supercells using machine-learned potentials, and how electronic properties can be predicted for large doped supercells using machine learning.

In this training, we will go over a robust and time efficient procedure for training and generating machine-learned forcefields (MLFFs) and how to apply these MLFFs on large-scale systems simulated in the LAMMPS molecular dynamics engine to compute materials properties. Additionally, we will cover new features related to find transition states of chemical reactions and NMR spectra using MedeA VASP.

Atomic-scale defects play a critical role in determining the functional properties and performance of many solid-state materials, with important implications for semiconductor doping, catalytic active sites, charge-carrier recombination and efficiency in solar photovoltaics, and ionic and electronic conductivity in battery materials. However, the dilute concentrations of point defects often make their experimental characterization extremely challenging. First-principles calculations provide a powerful complementary approach for investigating the structure, energetics, and electronic properties of defects at the atomic scale.
This training session will provide a systematic demonstration of density functional theory (DFT)-based workflows for investigating point defects in solids using MedeA. In particular, the session will cover: (1) exploration of defect configurational landscapes, including vacancies, interstitials, and antisites in multiple charge states using the ShakeNBreak approach; (2) DFT calculations of defect-containing supercells using MedeA VASP and the doped package integrated within MedeA; and (3) post-processing workflows for calculating and analyzing defect formation-energy diagrams, charge-transition levels, and defect and carrier concentrations. Cadmium telluride (CdTe), a technologically relevant solar photovoltaic material, will be used as a representative material system.

In this training, we will discuss various benefits of running a GRACE potential for determining material properties that are otherwise difficult to assess with DFT alone. In particular, the ability to determine the temperature-dependence of non-electronic and electronic properties will be discussed.