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.
We primarily focus on refractory nitrides, carbides and borides for hard coatings (TiN, HfN, NbN,..., their alloys), nitrides for high-energy density applications (materials with polymeric nitrogens). Our approach is to use ab-initio molecular dynamics simulations and static density functonal theory calculations (VASP, QE) together with machine learned interatomic potentials (MLIP). In this project we have three major objectives; i) calculating transport properties of multicomponent alloys, ii) continue the investigation of mechanical properties of refractory nitrides and nitride multilayers, ii) add more simulation data to our Hard coating Alloy Database (HADB) and iii) utilize neural network based approaches to learn/explore/predict properties of alloys. 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.