Atomic defects and dopants in ternary Z-phase transition-metal nitrides CrMN with M=V, Nb, Ta investigated with density functional theory

A density functional theory study of atomic defects and dopants in ternary Z-phase transition-metal nitrides CrMN with M=V, Nb, or Ta is presented. Various defect formation energies of native point defects and of substitutional atoms of other metal elements which are abundant in the steel as well, are evaluated. The dependence thereof on the thermodynamic environment, i.e. the chemical conditions of a growing Z-phase precipitate, is studied and different growth scenarios are compared. The results obtained may help to relate results of experimental atomic-scale analysis, by atom probe tomography or transmission electron microscopy, to the theoretical modeling of the formation process of the Z phase from binary transition metal nitrides

Rashba spin-orbit interaction in graphene armchair nanoribbons

We study graphene nanoribbons (GNRs) with armchair edges in the presence of Rashba spin-orbit interaction (RSOI). We impose the boundary conditions on the tight binding Hamiltonians for bulk graphene with RSOI by means of a sine transform and study in detail the influence of RSOI on the spectra and the spin polarization. We show that the spin polarization perpendicular to the GNR changes sign when reversing the momentum along the GNR if the bands are coupled by strong RSOI. Furthermore, we derive a linearized approximation to the RSOI Hamiltonian and find that only the neighboring modes of an energy band have to be taken into account in order to achieve a good approximation for the same band. Due to their experimental availability and various proposals for engineering appropriate RSOI, GNRs with armchair edges are a promising candidate for possible spintronics applications.Comment: added journal reference, small updates, 9 pages, 8 figure

Compositional optimization of hard-magnetic phases with machine-learning models

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML models to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models are obtained from a combinatorial high-throughput screening based on density-functional theory calculations. Our straightforward choice of describing the different configurations enables the subsequent use of the ML models for compositional optimization and thereby the prediction of promising substitutes of state-of-the-art magnetic materials like Nd$_2$Fe$_{14}$B with similar intrinsic hard-magnetic properties but a lower amount of critical rare-earth elements.Comment: 12 pages, 6 figure

Electrostatic treatment of charged interfaces in classical atomistic simulations

Artificial electrostatic potentials can be present in supercells constructed for atomistic simulations of surfaces and interfaces in ionic crystals. Treating the ions as point charges, we systematically derive an electrostatic formalism for model systems of increasing complexity, both neutral and charged, and with either open or periodic boundary conditions. This allows to correctly interpret results of classical atomistic simulations which are directly affected by the appearance of these potentials. We demonstrate our approach at the example of a strontium titanite supercell containing an asymmetric tilt grain boundary. The formation energies of charged oxygen vacancies and the relaxed interface structure are calculated based on an interatomic rigid-ion potential, and the results are analyzed in consideration of the electrostatic effects