Search

Abstract Accelerated design of hard-coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine learning models. We develop a heavily automated high-throughput workflow to build a database of industrially relevant hard-coating materials, such as binary and ternary nitrides. We use the high-throughput toolkit to automate the density functional theory calculation workflow. We present results, including elastic constants that are a key parameter determining mechanical properties of hard-coatings, for X 1−x Y x N ternary nitrides, where X,Y ∈ {Al, Ti, Zr, Hf} and fraction $$x=0,\frac{1}{4},\frac{1}{2},\frac{3}{4},1$$ x = 0 , 1 4 , 1 2 , 3 4 , 1 . We also explore ways for machine learning to support and complement the designed databases. We find that the crystal graph convolutional neural network trained on ordered lattices has sufficient accuracy for the disordered nitrides, suggesting that existing databases provide important data for predicting mechanical properties of qualitatively different types of materials, in our case disordered hard-coating alloys.

GaAs1 − xBix alloys attract significant interest due to their potentiality for several applications, including solar cells. Recent experiments link the crucial optical properties of these alloys to Bi clustering at certain Bi compositions. Using ab initio calculations, we show that there is no thermodynamical driving force for the formation of small GaBi clusters incorporating As substitutional sites. However, the Ga vacancies should gather Bi atoms leading to small Bi clusters, and the Ga vacancies can act as nucleation centers for phase separation. The formation energy of the GaAs1 − xBix with respect to GaAs and GaBi shows a maximum at intermediate Bi concentrations. Thermodynamics and kinetics of the GaAs1 − xBix film growth is discussed. High Bi solubility is obtained, if the Bi atoms on the energetically favorable atom positions in the subsurface layer are relatively frozen. The Ga vacancy concentration may be increased by the incorporation of Bi. The Bi atoms can also prevent the out diffusion of Ga vacancies. [ABSTRACT FROM AUTHOR]