We apply for a large scale NAISS allocation of super computer resources to carry out the computational part of the Thin Film Physics division materials science research at Linköping University with more than 15 theoretical researchers active in computation. We have demonstrated over a number of years that we use allocated resources in an efficient and productive manner resulting in a large number of scientific publications as well as recognition in the form of grants from VR, SSF, Linköping University, Swedish Government, KAW foundation, Swedish Energy Agency and more. Our computational environments continues to grow, such that this year it is logical to pinch off the Materials Design Division, who will apply independently for resources and independent line of research. Motivated by recent discoveries our core team has been gathering stronger momentum. Our academic output has also increased progressively each year. We thus request allocations in line with what is given to other large-scale projects. Our main software is VASP which is installed, optimized, scales well up to hundreds of cores, and is supported on all SNIC-centers where we apply for time. In the coming allocation period we will concentrate our efforts around these areas: 1. Properties and phase stability of new multifunctional materials. In particular boron rich alloys, borides, nitrides, carbides, oxides, high-entropy alloys, steels, metal/ceramic and molecular-nanolayer-based superlattices will be investigated in terms of phase stability and transformations, fracture resistance, defects, piezoelectric and thermoelectric properties, and spectroscopic response. Theoretical results, helped by the use of machine learning methods, will be subject to experimental verifications. 2. Method developments for disordered materials, thin film growth, and magnetic materials. We will continue developing our coupled atomistic spin dynamics – ab initio molecular dynamics method and apply it to steels and materials with magnetocaloric prospects. Employment of machine learning potentials will enable large-scale simulations with unprecedented accuracy for disordered materials and thin film growth. 3. Properties of superconducting materials. Engineering of superconducting properties in borides and carbides with strains from nanostructuring will be pursued, aiming at increasing the critical temperature of these compounds. Investigation of radiation hardness of unconventional superconductors (cuprates) will be carried out with machine learning potentials and radiation damage models for nuclear fusion applications. Our project has implications for societal needs in terms of energy harvesting and production, protective coatings, magnetic storage media, and neutron detector constructions at the ESS in Lund.