  Lithium ion batteries have been a central part of consumer electronics for
decades. More recently, they have also become critical components in the
quickly arising technological fields of electric mobility and intermittent
renewable energy storage. However, many fundamental principles and mechanisms
are not yet understood to a sufficient extent to fully realize the potential of
the incorporated materials. The vast majority of concurrent lithium ion
batteries make use of graphite anodes. Their working principle is based on
intercalation---the embedding and ordering of (lithium-) ions in the
two-dimensional spaces between the graphene sheets. This important process---it
yields the upper bound to a battery's charging speed and plays a decisive role
for its longevity---is characterized by multiple phase transitions, ordered and
disordered domains, as well as non-equilibrium phenomena, and therefore quite
complex. In this work, we provide a simulation framework for the purpose of
better understanding lithium intercalated graphite and its behaviour during use
in a battery. In order to address the large systems sizes and long time scales
required to investigate said effects, we identify the highly efficient, but
semi-empirical Density Funtional Tight Binding (DFTB) as a suitable approach
and combine particle swarm optimization (PSO) with the machine learning (ML)
procedure Gaussian Process Regression (GPR) to obtain the necessary parameters.
Using the resulting parametrization, we are able to reproduce experimental
reference structures at a level of accuracy which is in no way inferior to much
more costly ab initio methods. We finally present structural properties and
diffusion barriers for some exemplary system states.
