We apply for continuation of our large scale NAISS allocation of supercomputer resources in order to accelerate our successful research on advanced functional thin film materials at the very international front, based on both computational modeling of materials and method development. 15 theoretical and as many experimental scientists are active in this material science computational research coupled with laboratory testing and verification. Our track record over several years demonstrates that the allocated resources are used most efficiently and productivly, resulting in a large number of high-impact scientific publications and recognition, including grants from VR, SSF, Linköping University, Swedish Government Strategic Research Area, KAW Foundation, Swedish Energy Agency, as well as from joint appointments and interational scientific awards. Motivated by our discoveries made in the last year, the team proposes an increased research thrust. As our academic output increases, we request allocations on a level above what was granted in the last round, where we proved to be productive users. Our main softwares are VASP and LAMMPS, which are installed and optimized to scale well up to hundreds of cores, and are supported on all NAISS-centers to which we apply. In the coming allocation period we will concentrate our efforts on these areas: 1. Properties and phase stability of new multifunctional materials. In particular 2D noble metals, like our recently discovered goldene (published in Nature Synthesis), and boron-rich alloys, borides, nitrides, carbides, oxides, high-entropy alloys and compounds, 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, supported by machine learning methods (developed by us), will be subject to experimental verification. 2. Method developments for disordered materials, thin film growth, and magnetic materials. We will develop coupled atomistic spin dynamics – ab initio molecular dynamics method – and apply it to steels and materials with magnetocaloric prospects, together with high-throughput discovery of materials with strong spin-lattice coupling. Employment of machine learning potentials will enable yet larger-scale simulations with unprecedented accuracy for disordered materials and thin film growth. 3. Properties of novel superconducting materials. Engineering of superconducting properties in borides and carbides with strains from nanostructuring will be pursued, aiming at increasing the critical temperature. Investigation of radiation hardness, defect scattering and their annealing in unconventional superconductors (cuprates) will be carried out with first-principles calculations, machine learning potentials, and radiation damage models for nuclear fusion applications. The project resonates with societal needs in terms of energy harvesting and production, wear-resistant coatings, magnetic storage media, and neutron-detector applications at the ESS in Lund.