High-temperature thermal stability, elastic moduli and anisotropy are among the key properties, which are used in selecting materials for cutting and machining applications. The high computational demand of ab initio molecular dynamics (AIMD) simulations in calculating elastic constants of alloys promotes the development of alternative approaches. Machine learning concept grasped as hybride classical molecular dynamics and static first principles calculations have several orders less computational costs. Here we prove the applicability of the concept considering the recently developed moment tensor potentials (MTP), where moment tensors are used as materials descriptors which can be trained to predict the elastic constants of the prototypical hard coating alloy, Ti0.5Al0.5N at 900 K. We demonstrate excellent agreement between classical molecular dynamics simulations with MTPs and AIMD simulations. Moreover, we show that using MTPs one overcomes the inaccuracy issues present in approximate AIMD simulations of elastic constants of alloys.

The high-pressure synthesis of rhenium nitride pernitride with a crystal structure that is unusual for transition metal dinitrides and high values of hardness and bulk modulus attracted significant attention to this system. We investigate the thermodynamic and electronic properties of the P2(1)/c phase of ReN2 and compare them with two other polytypes, the C2/m and P4/mbm phases, suggested in the literature. Our calculations of the formation enthalpy at zero temperature show that the former phase is the most stable of the three up to a pressure p = 170 GPa, followed by the stabilization of the P4/mbm phase at higher pressure. The theoretical prediction is confirmed by diamond anvil cell synthesis of the P4/mbm ReN2 at approximate to 175 GPa. Considering the effects of finite temperature in the quasiharmonic approximation at p = 100 GPa we demonstrate that the P2(1)/c phase has the lowest free energy of formation at least up to 1000 K. Our analysis of the pressure dependence of the electronic structure of rhenium nitride pernitride shows the presence of two electronic topological transitions around 18 GPa, when the Fermi surface changes its topology due to the appearance of an electron pocket at the high-symmetry Y-2 point of the Brillouin zone while the disruption of the neck takes place slightly off from the Gamma-A line.

Data-driven approaches are becoming increasingly valuable for modern science, and they are making their way into industrial research and development (R&D). Supervised machine learning of statistical models can utilize databases of materials parameters to speed up the exploration of candidate materials for experimental synthesis and characterization. In this paper we introduce the HADB database, which contains properties of industrially relevant chemically disordered hard-coating alloys, focusing on their thermodynamic, elastic and mechanical properties. We present the technical implementations of the database infrastructure including support for browse, query, retrieval, and API access through the OPTIMADE API to make this data findable, accessible, interoperable, and reusable (FAIR). Finally, we demonstrate the usefulness of the database by training a graph -based machine learning (ML) model to predict elastic properties of hard-coating alloys. The ML model is shown to predict bulk and shear moduli for out out-of-sample alloys with less than 6 GPa mean average error.

Transition metal nitride alloys possess exceptional properties, making them suitable for cutting applications due to their inherent hardness or as protective coatings due to corrosion resistance. However, the computational demands associated with predicting these properties using ab initio methods can often be prohibitively high at the conditions of their operation at cutting tools, that is, at high temperatures and stresses. Machine learning approaches have been introduced into the field of materials modeling to address the challenge. In this paper, we present an active learning workflow to model the properties of our benchmark alloy system cubic B1 Ti0.5Al0.5N at temperatures up to 1500 K. With a minimal requirement of prior knowledge about the alloy system for our workflow, we train a moment tensor potential (MTP) to accurately model the material's behavior over the entire temperature range and extract elastic and vibrational properties. The outstanding accuracy of MTPs with relatively little training data demonstrates that the presented approach is highly efficient and requires about two orders of magnitude less computational resources than state-of-the-art ab initio molecular dynamics.

Wurtzite TmAlN (T-m = transition metal) themselves are of interest as semiconductors with tunable band gap, insulating motifs to superconductors, and piezoelectric crystals. Characterization of wurtzite TmAlN is challenging because of the difficulty to synthesize them as single-phase solid solution and such thermodynamic, elastic properties, and high temperature behavior of wurtzite Ti1-xAlxN is unknown. Here, we investigated the high temperature decomposition behavior of wurtzite Ti1-xAlxN films using experimental methods combined with first-principles calculations. We have developed a method to grow single-phase metastable wurtzite Ti1-xAlxN (x = 0.65, 0.75, 085, and 0.95) solid-solution films by cathodic arc deposition using low duty-cycle pulsed substrate-bias voltage. We report the full elasticity tensor for wurtzite Ti1-xAlxN as a function of Al content and predict a phase diagram including a miscibility gap and spinodals for both cubic and wurtzite Ti1-xAlxN. Complementary high-resolution scanning transmission electron microscopy and chemical mapping demonstrate decomposition of the films after high temperature annealing (950 degrees C), which resulted in nanoscale chemical compositional modulations containing Ti-rich and Al-rich regions with coherent or semicoherent interfaces. This spinodal decomposition of the wurtzite film causes age hardening of 1-2 GPa.