The groundbreaking research titled Combining ab‐initio and machine learning techniques for theoretical simulations of hard nitrides at extreme conditions opens new avenues in the study of hard nitrides. This thesis delves into the fusion of first-principles calculations with modern machine learning methods to explore nitride systems under extreme conditions effectively and accurately.
The study focuses on two key families of compounds: Ti1-xAlxN alloys, known for their exceptional properties in industrial cutting tools, and ReNx systems, which mimic crushing pressures akin to the earth’s inner core conditions. The traditional first-principles simulations of materials are typically conducted at zero temperature and pressure, which may not accurately reflect real-world scenarios. By combining machine learning techniques with high-accuracy data, this research aims to bridge the gap between theoretical simulations and practical applications.
One highlight of the study is the investigation of Ti1-xAlxN alloy coatings, which display remarkable age-hardening properties due to their unique composition. By utilizing machine learning interatomic potentials, researchers were able to achieve accurate results with significantly reduced computational demands compared to conventional methods. This innovative approach not only enhances our understanding of Ti1-xAlxN alloys but also paves the way for more efficient material studies in the future.
In addition to exploring the Ti1-xAlxN alloys, the research also delves into the intriguing world of metastable ReNx phases. These high-energy materials exhibit exceptional mechanical and electronic properties, reminiscent of naturally occurring diamond. By studying the various phases of ReNx compounds and analyzing their stability under different pressures, researchers aim to unlock the potential of these non-equilibrium compounds for future material advancements.
Overall, this thesis showcases the power of combining first-principles calculations with machine learning techniques to unravel the mysteries of hard nitrides at extreme conditions. By pushing the boundaries of computational simulations, researchers are not only advancing scientific knowledge but also opening doors to new possibilities in materials science and industrial applications.
