Within this project, we will address research questions ranging from disclosing functionality of novel materials discovered at extreme conditions to simulating hard coatings for cutting tools. We will model metallic alloys for rare-earth free high-performance permanent magnets, semiconductors for power electronics and quantum technologies. We will generate reliable data, make it available in our databases, and use it for training of AI/ML models relevant to real-life applications: interpretation and guidance of physical experiments and R&D in industry. We are developing and using advanced and efficient tools for materials modeling: first-principles theoretical simulations and AI/ML tools. Our simulations will allow for interpretation of experiments at large-scale facilities, ESRF, DESY and MAX-IV. The results obtained in the project will be relevant for several UN Sustainable Development Goals (SDGs), including Affordable and Clean Energy (SDG 7) and Industry, Innovation and Infrastructure (SDG 9). The project is organized in four work-packages (WP), reflecting our research supported by new and on-going grants from VR, KAW, VINNOVA, WISE and Horizon Europe, as well as by SRA AFM, SRA SeRC. We renew the structure of WPs in comparison to the on-going project NAISS2025/1-17 and formulate multiple novel tasks within each WP. In WP1 "Disclosing functionality of novel materials discovered at high-pressure high-temperature (HPHT) conditions" we will complete a systematic search for MCN5 compounds (where M is a transition metal, lanthanide, or actinide) based on the crystal structure of recently discovered CeCN5 and TbCN5. We will investigate the electronic properties of the 3D and 2D phases of the MCN5 compounds and the phase transitions mechanism for the metastable compounds. We will extend computational exploration of functional C–N frameworks towards complex quaternary systems, and we will continue to study binary compounds: recently synthesized metallic SnN5 hosting type-II Dirac fermions and Sn2N11 exhibiting metal-to-insulator transition. In WP2, we are working on the Hard Coating Alloys Database (HADB). The database is currently being extended over a diverse chemical space of periodic table focusing ternary nitride alloy compositions (AxB1−xN). Data generation will be further extended to quasi-ternary systems. Importantly, we will incorporate finite-temperature effects through training machine learning interatomic potentials (MLIPs). We will further develop Allegro ML potentials for high-accuracy calculations of elastic properties of single-crystal nitrides and carbides, while also studying mass transport of H impurities in metals and ceramics. In WP3 we will search for materials solutions for rare-earth free permanent magnets, power electronics and thermoelectrics. In this way we will participate in and contribute to the solution of UN SDG 7 Affordable and Clean Energy and SDG 9 Industry, Innovation and Infrastructure. We also investigate particular semiconductor defects of recent interest in quantum technologies. WP4 covers our developmental work and porting of existing codes onto GPU resources. This includes adaption of the TDEP code for NVIDIA GPUs and porting of in-house quantum spin dynamics software.