We apply for a 12 month NAISS large-computing allocation during 2025 (NAISS medium storage at NSC and PDC for computing space as well as at C3SE, for housing our overall vdW-DF-progress database, will be sought separately). The computing resources that we seek are to enable the research programs in the groups of the computing-active Chalmers architects of the van der Waals (vdW) density functional (vdW-DF) method for truly-nonlocal density functional theory (DFT). We apply to continue and significantly expand our present large computing NAISS2023-3-22 allocation, given that we have one Ph.D. student and one postdoc running plus (at least) two students working with us during 2025 as well as one, possibly two, postdoc arriving in January 2025. The postdocs will work on new projects, i.e., the Chalmers Spectra-Design Initiative and a Sweden-Korea collaboration of upgrading the most trusted metal-organic-framework (MOF) database (at KIST in Korea) with best-possible vdW-DF input on electronic structure. Our proposal is motivated by 2022-2024 progress suggesting exciting opportunities for our set of new range-separated hybrid vdW-DFs and associated possibilities for extracting MBPT-based approximations for the quasi-particles near the Frontier orbitals. In addition, we have and shall use our success at defining a new DFT of the in-situ electronegativity, again available by post-processing our new vdW-DFs at a standard-DFT-level of cost. With these accurate tools we intend to help the development of optical DNA markers and other bio-related applications (including catalysis). We shall also use our tools to improve MOFs for better function, optimizing their design to reduce pollution by gas filtering. This international collaboration involve database work and machine-learning of electronic-structure signatures of selective gas adsorption. The overall idea of the programs are to seek accelerated development, of DNA markers and of MOFs for green technology, by using best-possible and trusted modern nonlocal DFT (with extensions) in combination with database works and machine learning to assert if the desired and optimized function is likely to be there even before materials synthesis is attempted.