Title:	Database for developing, testing, understanding, and launching vdW-DF-method progress
DNr:	NAISS 2024/6-432
Project Type:	NAISS Medium Storage
Principal Investigator:	Per Hyldgaard <hyldgaar@chalmers.se>
Affiliation:	Chalmers tekniska högskola
Duration:	2025-01-01 – 2026-01-01
Classification:	10304 10302 10407
Homepage:	http://fy.chalmers.se/~hyldgaar/SNIC
Keywords:

Abstract

This application concerns storage that we need to succeed at our overall goals: Developing the van der Waals (vdW) density functional (vdW-DF) method and using it for green technology solutions as well as for Spectral Design work within the Chalmers Nano Excellent initiative on DNA fluorescence markers. Some background is provided in the report for NAISS/3-306 (which this proposal extends) as well as in the application for a NAISS-Large-Compute allocation NAISS2024/3-16 (related to 2025). We seek medium-size storage for two purposes. First is to secure sufficient disk space (Buffers of 10-20 TBs and millions of files) to actually utilize our NAISS large-compute allocation, each at NSC and at PDC. Second is to allow us to continue to grow our vdW-DF database (with 40 TB located at C3SE and an extra 20-30 TB will be needed for that at PDC). This is because these databases of complete new (quasiparticle-ready) DFT contains information that directly accelerate our research on DNA spectra and on green technology problems, for example, as discussed in the most recent storage and compute progress reports and as detailed also in the application for our NAISS2024/3-16 compute allocation. In short, we now see clear benefits from now dramatically expanding the information-content of what we keep for some classes of individual database entries. This is, for example, true for ongoing work to improve DNA markers, something that we shall in 2025 intensify within the new Chalmers Spectral Design Initiative that we are starting (where in addition to the presently listed participants a new postdoc will start in with the chemistry-theory participant in January, but will learn to expand his experience in using large-scale computing from me).