In this thesis I focus on combining the high accuracy of first-principles calculations with modern machine learning methods to make large scale investigations of industrially relevant nitride systems reliable and computationally viable. I study the electronic, thermodynamic and mechanical properties of two families of compounds: Ti_1−xAl_xN alloys at the operational conditions of industrial cutting tools and ReN_x systems at crushing pres-sures comparable to inner earth core conditions. Standard first-principles simulations of materials are usually carried out at zero temperature and pressure, and while many state-of-the-art approaches can take these effects into account, they are usually accompanied by a substantial increase in computational demand. In this thesis I therefore explore the possiblities of studying materials at extreme conditions using machine learning methods with extraordinary efficiency without loss of calculational accuracy. 

Ti_1−xAl_xN alloy coatings exhibit exceptional properties due to their inherent ability to spinodally decompose at elevated temperature, leading to age-hardening. Since the cubic B1 phase of Ti_1−xAl_xN is well-studied, available high-accuracy first-principles data served as both a benchmark and data set on which to train a machine learning interatomic potential. Using the reliable moment tensor potentials, an investigation of the accuracy and efficiency of this approach was carried out in a machine learning study. Building upon the success of this technique, implementation of a learning-on-the-fly (active learning) methodology into a workflow to determine accurate material properties with minimal prior knowledge showed great promise, while maintaining a computational demand up to two orders of magnitude lower than comparable first-principles approaches. Investigations of properties of industrially lesser desired, but sometimes present hexagonal alloy phases of Ti_1−xAl_xN are also included in this thesis, since knowledge and understanding of all competing phases can help guide development toward improving cutting tool lifetime and performance. Furthermore, while w-Ti_1−xAl_xN may not be able to compete with its cubic counterpart in terms of hardness, it shows promise for other applications due to its electronic and elastic properties. 

Metastable ReN_x phases are high energy materials due to their covalent N-N and Re-N bonds, leading to exceptional mechanical and electronic properties. Just like diamond, the hardest and arguably most famous metastable mate-rial naturally occurring on earth, they are stabilized by extreme pressures and high temperatures, but can be quenched to ambient conditions. Understanding the formation and existence of these non-equilibrium compounds may hold the key to unlocking a new generation of hard materials. In this thesis, all currently known phases of ReN_x compounds have been investigated, encompassing both experimentally observed and theoretically suggested structures. Investigations of the convex hulls across a broad pressure range were carried out, coupled with calculations of phonons in the proposed crystals to determine both energetic and dynamical stability. Overall, the studies included in this thesis focused mainly on investigation of the ground state of ReN₂ at higher pressure, where experimental results were deviating from earlier theoretical predictions. Additional research focused on specifically exploring properties and stability of novel ReN₆ at synthesis conditions using the active learning workflow to train an interatomic potential. 

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.