From Multiple Scale Modeling to Multiscale-Modeling

Licentiate thesis, 2021

To power a sustainable future, interest in battery research and technology is at an all time high. In order to enable a transition to green tech many applications, such as the automotive industry, is in need of higher power densities, energy densities, longer life-times, and safer batteries.\\

One crucial component of batteries is the electrolyte, which for lithium-ion batteries (LIBs) have not developed as much as one would expect since its introduction in the 1990s. Through the use of novel electrolyte concepts such as highly concentrated electrolytes (HCE) and localized highly concentrated electrolytes (LHCE) desired qualities such as an increased energy density could be achieved. The effects of local properties on macroscopic behaviour within these systems are much more striking than conventional LIB electrolytes, constraining the use of common simulation techniques used in battery research.

This thesis studies these novel electrolyte concepts using an array of different computational methods, such as DFT, AIMD, and classical MD. Based on these techniques, as well as on the CHAMPION method, the work done in this thesis attempts to develop a method for tying together understanding of materials physics at the different scales represented by AIMD and classical MD through force sampling. This force sampling is presented as an alternative to commonplace MD force fields such as AMBER, CHARMM and GROMACS.

Finding the local structure important for explaining global transport phenomenon by showing that local HCE structure is retained when going from HCE to LHCE as well as showing the possibility for these new types of FFs, even though more work is needed on the accuracy of these FFs.