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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]

The thermodynamic ordering transformation of tetragonal FeNi system is investigated by the Exact Muffin-Tin Orbitals (EMTO) method. The tetragonal distortion of the unit cell is taken into account and the free energy is calculated as a function of long-range order and includes the configurational, vibrational, electronic and magnetic contributions. We find that both configurational and vibrational effects are important and that the vibrational effect lowers the predicted transformation temperature by about 480 K compared to the value obtained merely from the configurational free energy. The predicted temperature is in excellent agreement with the experimental value when all contributions are taken into account. We also perform spin dynamics calculations for the magnetic transition temperature and find it to be in agreement with the experiments. The present research opens new opportunities for quantum-mechanical engineering of the chemical and magnetic ordering in tetrataenite.

We implement a Becke fuzzy cells type space partitioning scheme for the purposes of exchange-correlation within the GPAW projector augmented-wave method based density functional theory code. Space partitioning is needed in the situation where one needs to treat different parts of a combined system with different exchange-correlation functionals. For example, bulk and surface regions of a system could be treated with functionals that are specifically designed to capture the distinct physics of those regions. Here, we use the space partitioning scheme to implement the quasi-nonuniform exchange-correlation scheme, which is a useful practical approach for calculating metallic alloys on the generalized gradient approximation level. We also confirm the correctness of our implementation with a set of test calculations.

Semilocal density functional approximations occupy the second rung of the Jacob's ladder model and are thus expected to have certain limits to their applicability. A recent study [Y. Zhang, G. Kresse, and C. Wolverton, Phys. Rev. Lett. 112, 075502 (2014)] hypothesizes that the formation energy, being one of the key quantities in alloy theory, would be beyond the grasp of semilocal density functional theory (DFT). Here, we explore the physics of semilocal DFT formation energies and shed light on the connection between the accuracy of the formation energy and the ability of a semilocal approximation to produce accurate lattice constants. We demonstrate that semilocal functionals designed to perform well for alloy constituents can concomitantly solve the problem of alloy formation energies.

We determine the interface energy and the work of separation of the Fe/Cr2O3 interface using first-principles density functional theory. Starting from different structures, we put forward a realistic interface model that is suitable to study the complex metal-oxide interaction. This model has the lowest formation energy and corresponds to an interface between Fe and oxygen terminated Cr2O3. The work of separation is calculated to be smaller than the intrinsic adhesion energy of pure Fe or Cr2O3, suggesting that stainless steel surfaces should preferentially break along the metal-oxide interface. The relative stabilities and magnetic interactions of the different interfaces are discussed. Next we introduce Cr atoms into the Fe matrix at different positions relative to the interface. We find that metallic Cr segregates very strongly to the (FeCr)/Cr2O3 interface, and increases the separation energy of the interface, making the adhesion of the oxide scale mechanically more stable. The Cr segregation is explained by the enthalpy of formation.