Atomic-scale origin of the low grain-boundary resistance in perovskite solid electrolyte Li0.375Sr0.4375Ta0.75Zr0.25O3

Abstract

Oxide solid electrolytes (OSEs) have the potential to achieve improved safety and energy density for lithium-ion batteries, but their high grain-boundary (GB) resistance generally is a bottleneck. In the well-studied perovskite oxide solid electrolyte, Li3xLa2/3-xTiO3 (LLTO), the ionic conductivity of grain boundaries is about three orders of magnitude lower than that of the bulk. In contrast, the related Li0.375Sr0.4375Ta0.75Zr0.25O3 (LSTZ0.75) perovskite exhibits low grain boundary resistance for reasons yet unknown. Here, we use aberration-corrected scanning transmission electron microscopy and spectroscopy, along with an active learning moment tensor potential, to reveal the atomic scale structure and composition of LSTZ0.75 grain boundaries. Vibrational electron energy loss spectroscopy is applied for the first time to reveal atomically resolved vibrations at grain boundaries of LSTZ0.75 and to characterize the otherwise unmeasurable Li distribution therein. We find that Li depletion, which is a major reason for the low grain boundary ionic conductivity of LLTO, is absent for the grain boundaries of LSTZ0.75. Instead, the low grain boundary resistivity of LSTZ0.75 is attributed to the formation of a nanoscale defective cubic perovskite interfacial structure that contained abundant vacancies. Our study provides new insights into the atomic scale mechanisms of low grain boundary resistivity.

Lithium dynamics at grain boundaries of β-Li3PS4 solid electrolyte

“…Li ion diffusivity values at the GB region and the amorphous/crystal interface are extracted using a code that is developed by the current authors. 62,63…”

Going against the Grain: Atomistic Modeling of Grain Boundaries in Solid Electrolytes for Solid-State Batteries

“…As a result, all of the chemical behavior is learned from the reference data. Powerful and accurate MLFFs have been developed for a range of topical solid electrolyte materials. − Several exhaustive reviews of MLFFs and their development have been published in recent years. − …”

“…This helped to rationalize why previously calculated activation energies for bulk LLTO have been consistently underestimated compared to experiment, similar to the above studies for other oxide solid electrolytes. In a more recent study, Lee et al 54 studied the impact of GBs on Li-ion transport in another oxide perovskite, Li 0.375 Sr 0.4375 Ta 0.75 Zr 0.25 O 3 (LSTZ0.75), which, unlike LLTO, exhibits low GB resistance. Specifically, the authors used hybrid Monte Carlo/MD simulations enabled by an MLFF to investigate the structures and compositions of GBs in LSTZ0.75 to understand why they do not significantly reduce Li-ion transport, as is typical oxide solid electrolytes.…”

Modeling Chemical Exfoliation of Non-van der Waals Chromium Sulfides by Machine Learning Interatomic Potentials and Monte Carlo Simulations

“…Machine learning potentials (MLPs) have recently emerged as highly promising tools in computational materials science due to their near-DFT accuracy, nearly linear scaling with system size, and exceptional transferability to diverse chemical environments. Prominent examples of MLPs include neural network potentials (NNPs), − Gaussian approximation potentials, , moment tensor potentials, spectral neighbor analysis potentials, atomic CE potentials, , and graph NNPs. − The high flexibility of MLPs allows for broad applicability across different types of matter, encompassing bulk − and 2D crystals, amorphous materials, , liquids, − interfaces, , and clusters. , In the domain of disordered systems, MLPs were employed to investigate binary alloys spanning a wide range of compositions, − high-entropy alloys, − and grain boundaries. − However, a conspicuous gap exists in the literature concerning the application of MLPs to nonstoichiometric systems characterized by varying elevated vacancy concentrations. Herein, we address this gap by examining the efficacy of NNPs for modeling nonstoichiometric chromium sulfides, a material that has not been explored in the existing literature using either machine learning or conventional potentials.…”