Zheng Zhuoyuan, Zhou Jie, Zhu Yusong
School of Energy Science and Engineering, Nanjing Tech University, Nanjing, Jiangsu Province 211816, China.
Chem Soc Rev. 2024 Mar 18;53(6):3134-3166. doi: 10.1039/d3cs00572k.
The increasing demand for high-security, high-performance, and low-cost energy storage systems (EESs) driven by the adoption of renewable energy is gradually surpassing the capabilities of commercial lithium-ion batteries (LIBs). Solid-state electrolytes (SSEs), including inorganics, polymers, and composites, have emerged as promising candidates for next-generation all-solid-state batteries (ASSBs). ASSBs offer higher theoretical energy densities, improved safety, and extended cyclic stability, making them increasingly popular in academia and industry. However, the commercialization of ASSBs still faces significant challenges, such as unsatisfactory interfacial resistance and rapid dendrite growth. To overcome these problems, a thorough understanding of the complex chemical-electrochemical-mechanical interactions of SSE materials is essential. Recently, computational methods have played a vital role in revealing the fundamental mechanisms associated with SSEs and accelerating their development, ranging from atomistic first-principles calculations, molecular dynamic simulations, multiphysics modeling, to machine learning approaches. These methods enable the prediction of intrinsic properties and interfacial stability, investigation of material degradation, and exploration of topological design, among other factors. In this comprehensive review, we provide an overview of different numerical methods used in SSE research. We discuss the current state of knowledge in numerical auxiliary approaches, with a particular focus on machine learning-enabled methods, for the understanding of multiphysics-couplings of SSEs at various spatial and time scales. Additionally, we highlight insights and prospects for SSE advancements. This review serves as a valuable resource for researchers and industry professionals working with energy storage systems and computational modeling and offers perspectives on the future directions of SSE development.
可再生能源的采用推动了对高安全性、高性能和低成本储能系统（EES）的需求不断增加，这正逐渐超越商用锂离子电池（LIB）的能力。包括无机物、聚合物和复合材料在内的固态电解质（SSE）已成为下一代全固态电池（ASSB）的有前途的候选材料。ASSB具有更高的理论能量密度、更好的安全性和更长的循环稳定性，使其在学术界和工业界越来越受欢迎。然而，ASSB的商业化仍面临重大挑战，如界面电阻不理想和枝晶快速生长。为了克服这些问题，深入了解SSE材料复杂的化学 - 电化学 - 机械相互作用至关重要。最近, 计算方法在揭示与SSE相关的基本机制和加速其发展方面发挥了至关重要的作用，范围从原子第一性原理计算、分子动力学模拟、多物理场建模到机器学习方法。这些方法能够预测固有特性和界面稳定性、研究材料降解以及探索拓扑设计等。在这篇全面的综述中，我们概述了SSE研究中使用的不同数值方法。我们讨论了数值辅助方法的当前知识状态，特别关注基于机器学习的方法，以了解SSE在各种空间和时间尺度上的多物理场耦合。此外，我们强调了SSE进展的见解和前景。这篇综述为从事储能系统和计算建模的研究人员和行业专业人士提供了宝贵的资源，并提供了SSE发展未来方向的观点。
