DFTB Modeling of Lithium-Intercalated Graphite with Machine-Learned Repulsive Potential.

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 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 in its longevity, is characterized by multiple phase transitions, ordered and disordered domains, as well as nonequilibrium phenomena, and therefore quite complex. In this work, we provide a simulation framework for the purpose of better understanding lithium-intercalated graphite and its behavior during use in a battery. To address large system sizes and long time scales required to investigate said effects, we identify the highly efficient, but semiempirical density functional tight binding (DFTB) as a suitable approach and combine particle swarm optimization (PSO) with the machine learning (ML) procedure Gaussian process regression (GPR) as implemented in the recently developed GPrep package for DFTB repulsion fitting 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.

Li-ion half-cells studied operando during cycling by small-angle neutron scattering.

Small-angle neutron scattering (SANS) was recently applied to the in situ and operando study of the charge/discharge process in Li-ion battery full-cells based on a pouch cell design. Here, this work is continued in a half-cell with a graphite electrode cycled versus a metallic lithium counter electrode, in a study conducted on the SANS-1 instrument of the neutron source FRM II at the Heinz Maier-Leibnitz Zentrum in Garching, Germany. It is confirmed that the SANS integrated intensity signal varies as a function of graphite lithiation, and this variation can be explained by changes in the squared difference in scattering length density between graphite and the electrolyte. The scattering contrast change upon graphite lithiation/delithiation calculated from a multi-phase neutron scattering model is in good agreement with the experimentally measured values. Due to the finite coherence length, the observed SANS contrast, which mostly stems from scattering between the (lithiated) graphite and the electrolyte phase, contains local information on the mesoscopic scale, which allows the development of lithiated phases in the graphite to be followed. The shape of the SANS signal curve can be explained by a core-shell model with step-wise (de)lithiation from the surface. Here, for the first time, X-ray diffraction, SANS and theory are combined to give a full picture of graphite lithiation in a half-cell. The goal of this contribution is to confirm the correlation between the integrated SANS data obtained during operando measurements of an Li-ion half-cell and the electrochemical processes of lithiation/delithiation in micro-scaled graphite particles. For a deeper understanding of this correlation, modelling and experimental data for SANS and results from X-ray diffraction were taken into account.