A Holistic Take on Simulating Battery Electrolytes

Doctoral thesis, 2023

As powering a sustainable future is a global goal, interest in battery research and technology is at an all-time high. In order to enable a transition to green-tech, many industries, such as the automotive industry, urge for batteries with higher power and energy densities, longer life-times, and that are safer. All these properties are fundamentally limited by the materials employed. Hence humanity’s ability to create new energy storage materials need to improve.

The way energy storage materials have been developed up until now have mainly been in the lab. With many other industries benefiting from IT tools the battery industry is seeing a need for new better computational tools to aid in developing new materials. Many put their faith in machine learning algorithms to provide the solution, but those methods are not flawless, and are especially hard to work with when modeling electrolytes. This thesis focuses on physics-based methods to model battery electrolytes, such as DFT, AIMD, and classical MD, and makes a holistic retake on how these methods could be used in unison to better help material developers screen their materials.

Novel electrolyte concepts such as highly concentrated electrolytes and localized highly concentrated electrolytes, both for lithium and calcium batteries, are studied using the aforementioned tools. This thesis also presents how the newly developed CHAMPION software and methods can be used to tie the dierent methods together and possibly also extend their use by mapping forces on identified interatomic interactions, which may enable much faster turn-around in the simulation protocols.