Shaping the Future of Solid‐State Electrolytes through Computational Modeling

Abstract

Advances and progress in computational research that aims to understand and improve solid‐state electrolytes (SSEs) are outlined. One of the main challenges in the development of all‐solid‐state batteries is the design of new SSEs with high ion diffusivity that maintain chemical and phase stability and thereby provide a wide electrochemical stability window. Solving this problem requires a deep understanding of the diffusion mechanism and properties of the SSEs. A second important challenge is the development of an understanding of the interface between the SSE and the electrode. The role of molecular simulations and modeling in dealing with these challenges is discussed, with reference to examples in the literature. The methods used and issues considered in recent years are highlighted. Finally, a brief outlook about the future of modeling in studying solid‐state battery technology is presented.

Diffusion of lithium ions in Lithium-argyrodite solid-state electrolytes

“…In order to avoid difficulties in development of force fields for these systems where charge transfer and polarizability are likely to play a role, ab initio molecule dynamics simulations are attractive and have been widely employed. However, because of the relatively low conductivity of solidstate electrolyte materials (usually lower than 10 −3 S cm −1 ), in most cases it is very computationally expensive, and sometimes impossible, to directly calculate an accurate value for conductivity of the materials at room temperature using ab initio molecular dynamics simulations 14,15 . One way to solve this problem is to calculate the diffusion coefficient at high temperatures and use the Arrhenius relation to predict a value for ionic diffusion at room temperature 14,16 .…”

“…Li 6 PS 5 Cl is an argyrodite electrolyte for which there has been much experimental and computational research. However, due to various difficulties mentioned above, the diffusion mechanism in this material is not fully understood and the predictions of the conductivity using computational and experimental results vary over orders of magnitude 15,16,23,[26][27][28][29] .…”

Progress and perspective of the cathode/electrolyte interface construction in all‐solid‐state lithium batteries

“…209 As volumetric strain of the cathode active materials during the cycling process usually results in the problem of "contact loss" at the cathode/solid-state electrolyte interface, the investigation of fracture behaviors, especially the electro-chemo-mechanics of electrodes, using FEM simulation is also noteworthy. [210][211][212][213][214] Moreover, some emerging simulation methods in the renewable energy field, such as machine learning, [215][216][217][218] the phase-field model, 219 and the CALPHAD approach, 220 are promising for the prediction and description of the cathode/solidstate electrolyte interfacial behavior in the future. LSV and CV [202] Abbreviations: CV, cyclic voltammetry; LSV, linear sweep voltammetry; RT, room temperature.…”

Quantitative Mapping of Molecular Substituents to Macroscopic Properties Enables Predictive Design of Oligoethylene Glycol-Based Lithium Electrolytes

“…Although this approach has led to significant advances, it cannot provide a priori quantitative predictions of the properties of novel electrolytes. 9 − 14 A quantitative model for understanding the impacts of molecular substituents on bulk lithium electrolyte properties will significantly advance the development of next-generation energy storage devices that require molecularly optimized electrolytes with high ionic conductivities 15 at room temperature, e.g., on par with current liquid carbonate electrolytes (∼10 mS cm –1 at 298 K) or solid-state ceramics (e.g., a sulfide-based superionic conductor with ionic conductivity of 25 mS cm –1 at 298 K), 16 yet with improved safety and processability profiles. 17 Moreover, the approach to developing such a quantitative model could be broadly applied to other complex materials systems where molecular features drive macroscopic function.…”

“…In materials-based systems, however, quantitatively predicting how molecular modifications will translate across length scales to yield macroscopic property changes is especially challenging. , For example, in the field of organic lithium conducting electrolytes, where ion transport is controlled by a dynamic ensemble of all microscopic structures formed between the electrolyte solvent, a dissolved lithium salt, and other possible additives, novel electrolyte components are often designed using chemical intuition and Edisonian (i.e., trial and error) optimization. Although this approach has led to significant advances, it cannot provide a priori quantitative predictions of the properties of novel electrolytes. − A quantitative model for understanding the impacts of molecular substituents on bulk lithium electrolyte properties will significantly advance the development of next-generation energy storage devices that require molecularly optimized electrolytes with high ionic conductivities at room temperature, e.g., on par with current liquid carbonate electrolytes (∼10 mS cm –1 at 298 K) or solid-state ceramics (e.g., a sulfide-based superionic conductor with ionic conductivity of 25 mS cm –1 at 298 K), yet with improved safety and processability profiles . Moreover, the approach to developing such a quantitative model could be broadly applied to other complex materials systems where molecular features drive macroscopic function.…”