The goal of the project is to provide computational resources to the FunMat-II competence center. The scientific goal is to establish a platform of high accuracy ab-initio molecular dynamics simulations combined with active machine learning of force fields to investigate high temperature thermodynamic, mechanical properties of materials and rare diffusion events. Furthremore, we are aiming to extend our HADB database with materials relevant for hard coatings. We primarily focus on refractory nitrides, carbides and borides for hard coatings (TiN, HfN, NbN,..., their alloys), bcc high-entropy metallic alloys. Our approach is based on HTTK package to use ab-initio molecular dynamics simulations and static density functonal theory calculations (utilizing VASP, QE software packages) together with machine learned interatomic potentials (MLIP). In this project we have four major objectives; i) continue the investigation of mechanical properties of refractory nitrides and nitride multilayers using machine learned interatomic potentials, ii) add more data (Nb, Ta and Si containing alloys) to our Hard coating Alloy Database (HADB) using a workflow with HTTK software package and iii) utilize neural network based approaches to learn/explore/predict properties of alloys using HADB. Our last objective is to initiate the usage of atomic cluster expansion (ACE) method to develop MLIPs and make a comparison with MTP based MLIPs. Through the FunMat-II competence center we are in close collaboration with Seco Tools and Sandik Coromant. We also collaborate world-leading experimentalists in the filed of high-pressure physics. Our research is supported by the Swedish strategic FunMat-II consortium and the Interdisciplinary Laboratory for Advanced Functional Materials (AFM) at Linköping University. The ab-initio calculations will be performed using VASP and Quantum Espresso (QE). The classical molecular dynamics simulations will be done using LAMMPS.