NMR Study of LICXGraphite Intercalation Compounds Prepared Under High Pressure

Hybrid Machine Learning-Enabled Potential Energy Model for Atomistic Simulation of Lithium Intercalation into Graphite from Plating to Overlithiation

Graphite is one of the most widely used negative electrode materials for lithium ion batteries (LIBs). However, because of the rapid growth of demands pursuing higher energy density and charging rates, comprehensive insights into the lithium intercalation and plating processes are critical for further boosting the potential of graphite electrodes. Herein, by utilizing the dihedral-angle-corrected registry-dependent potential (DRIP) (Wen et al., Phys. Rev. B 2018, 98, 235404), the Ziegler-Biersack-Littmark (ZBL) potential (Ziegler and Biersack, Astrophysics, Chemistry, and Condensed Matter; 1985, pp 93-129), and the machine learning-based spectral neighbor analysis (SNAP) potential (Thompson et al., J. Comput, Phys. 2015, 285, 316-330), we have successfully trained a hybrid machine learning-enabled potential energy model capable of simulating a wide spectrum of lithium intercalation scenario from plating to overlithiation. Our extensive atomistic simulations reveal the trapping of intercalated lithium atoms close to the graphite edges due to high hopping barriers, resulting in lithium plating. Furthermore, we report a stable dense graphite intercalation compound (GIC) LiC₄ with a theoretical capacity of 558 mAh/g, wherein lithium atoms occupy alternating upper/lower graphene hollow sites with a nearest Li-Li distance of 2.8 Å. Surprisingly, following the same lithium insertion manner would allow the nearest Li-Li distance to be retained until the capacity reaches 845.2 mAh/g, corresponding to a GIC of LiC_2.6. Hence, the present study demonstrates that the hybrid machine learning approach could further extend the scope of machine learning energy models, allowing us to investigate the lithium intercalation into graphite over a wide range of intercalation capacity to unveil the underlying mechanisms of lithium plating, diffusion, and discovery of new dense GICs for advanced LIBs with high charging rates and high energy densities.

Unveiling the stability of Sn/Si/graphite composites for Li-ion storage by physical, electrochemical and computational tools

Complex materials composed of two and three elements with high Li-ion storage capacity are investigated and tested as lithium-ion battery (LiB) negative electrodes. Namely, anodes containing tin, silicon, and graphite show very good performance because of the large gravimetric and volumetric capacity of silicon and structural support provided by tin and graphite. The performance of the composites during the first cycles was studied using ex situ magic angle spinning (MAS) ⁷Li Nuclear Magnetic Resonance (NMR), density functional theory (DFT) calculations, and electrochemical techniques. The best performance was obtained for Sn/Si/graphite in a 1 : 1 : 1 proportion, due to an emergent effect of the interaction between Sn and Si. The results suggest a stabilization effect of Sn over Si, providing a physical constraint that prevents Si pulverization. This mechanism ensures good cyclability over more than one hundred cycles, low capacity fading and high specific capacity.

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.