# DFTB modelling of lithium intercalated graphite with machine-learned repulsive potential

## TL;DR

This paper develops a simulation framework using DFTB combined with machine learning to study lithium intercalation in graphite, enabling efficient modeling of battery materials with high accuracy.

## Contribution

It introduces a novel combination of DFTB, particle swarm optimization, and Gaussian Process Regression to accurately model lithium intercalated graphite.

## Key findings

- Reproduces experimental structures with high accuracy
- Provides structural properties of lithium intercalation
- Calculates diffusion barriers for battery-relevant states

## Abstract

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.