We apply for a NAISS Large Compute allocation of super-computer resources to carry out the computational part of the Materials Design division materials science research at Linköping University with more than 10 theoretical researchers active in computation. We have for more than decade been part of another large compute project (SNIC 2022/1-5, Thin Film Physics Division) where we have demonstrated use of 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, ERC, the KAW foundation, SSF, WISE, Linköping University, and more.
In July 2021 we left the Thin Film Physics division and formed the Materials Design division. We have commonly agreed to apply for separate Large Compute allocation due different research activities and needs. Our computational team has increased in size in recent years and will continue to do so with new recruits joining soon, as have our scientific output. Due to more advanced computational methods, in particular ab-initio crystal structure predictions, limitations in supercomputer allocations are an issue for us. Therefore, we 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 NAISS-centers where we apply for time.
In the coming allocation period, we will concentrate our efforts corresponding to our ongoing research supported by grants from VR, ERC, SSF, KAW, Göran Gustafson, and WISE. This proposal deals with theory and simulations of advanced functional materials for green energy and emerging technologies. The planned project can be divided up into several sub-projects:
• Prediction of new complex multifunctional materials through use of first-principles and evolutionary algorithms for fundamental understanding of stability, crystal structure and properties, needed for development of materials for sustainable technologies.
• Continue our broad study to identify the next generation of 2D materials from chemical exfoliation and the impact from surface chemistry on the 2D material’s properties by combining first-principles calculations with machine-learning methodologies.
• Chemical reactions on surfaces including studies of 2D carbides and other 2D materials for catalysis, energy storage/conversion, and investigations of how organic molecules react on metal surfaces.
We will also explore data-mining algorithms for the search of novel functional materials. Our computational need is quite high concerning the complexity of the topics and the number of users involved. The application is intended for our most time consuming and demanding applications, but also for calculations of intermediate size, that have to be done at higher frequency. By utilizing NAISS supercomputer resources, we will employ efficient tools for materials modeling to guide and support materials design.