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Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries
Chemical Reviews ( IF 51.4 ) Pub Date : 2022-05-16 , DOI: 10.1021/acs.chemrev.1c00904
Nan Yao 1 , Xiang Chen 1 , Zhong-Heng Fu 1 , Qiang Zhang 1
Chemical Reviews ( IF 51.4 ) Pub Date : 2022-05-16 , DOI: 10.1021/acs.chemrev.1c00904
Nan Yao 1 , Xiang Chen 1 , Zhong-Heng Fu 1 , Qiang Zhang 1
Affiliation
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Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems in the future. The liquid electrolyte is one of the most important parts of a battery and is extremely critical in stabilizing the electrode–electrolyte interfaces and constructing safe and long-life-span batteries. Tremendous efforts have been devoted to developing new electrolyte solvents, salts, additives, and recipes, where molecular dynamics (MD) simulations play an increasingly important role in exploring electrolyte structures, physicochemical properties such as ionic conductivity, and interfacial reaction mechanisms. This review affords an overview of applying MD simulations in the study of liquid electrolytes for rechargeable batteries. First, the fundamentals and recent theoretical progress in three-class MD simulations are summarized, including classical, ab initio, and machine-learning MD simulations (section 2). Next, the application of MD simulations to the exploration of liquid electrolytes, including probing bulk and interfacial structures (section 3), deriving macroscopic properties such as ionic conductivity and dielectric constant of electrolytes (section 4), and revealing the electrode–electrolyte interfacial reaction mechanisms (section 5), are sequentially presented. Finally, a general conclusion and an insightful perspective on current challenges and future directions in applying MD simulations to liquid electrolytes are provided. Machine-learning technologies are highlighted to figure out these challenging issues facing MD simulations and electrolyte research and promote the rational design of advanced electrolytes for next-generation rechargeable batteries.
中文翻译：
将经典、Ab Initio 和机器学习分子动力学模拟应用于可充电电池的液体电解质
可充电电池已成为我们日常生活中不可或缺的工具，被认为是未来构建可持续能源系统的有前途的技术。液体电解质是电池最重要的部件之一，对于稳定电极-电解质界面和构建安全和长寿命的电池至关重要。人们一直致力于开发新的电解质溶剂、盐、添加剂和配方，其中分子动力学 (MD) 模拟在探索电解质结构、离子电导率等物理化学性质和界面反应机制方面发挥着越来越重要的作用。本综述概述了在可充电电池液体电解质研究中应用 MD 模拟。第一的，ab initio和机器学习 MD 模拟（第 2 节）。接下来，将 MD 模拟应用于液体电解质的探索，包括探测体积和界面结构（第 3 节），推导宏观性质，如电解质的离子电导率和介电常数（第 4 节），并揭示电极-电解质界面反应机制（第 5 节）依次介绍。最后，提供了关于将 MD 模拟应用于液体电解质的当前挑战和未来方向的一般性结论和有见地的观点。重点强调机器学习技术，以解决 MD 模拟和电解质研究面临的这些具有挑战性的问题，并促进下一代可充电电池先进电解质的合理设计。
更新日期：2022-05-16
中文翻译：
将经典、Ab Initio 和机器学习分子动力学模拟应用于可充电电池的液体电解质
可充电电池已成为我们日常生活中不可或缺的工具，被认为是未来构建可持续能源系统的有前途的技术。液体电解质是电池最重要的部件之一，对于稳定电极-电解质界面和构建安全和长寿命的电池至关重要。人们一直致力于开发新的电解质溶剂、盐、添加剂和配方，其中分子动力学 (MD) 模拟在探索电解质结构、离子电导率等物理化学性质和界面反应机制方面发挥着越来越重要的作用。本综述概述了在可充电电池液体电解质研究中应用 MD 模拟。第一的，ab initio和机器学习 MD 模拟（第 2 节）。接下来，将 MD 模拟应用于液体电解质的探索，包括探测体积和界面结构（第 3 节），推导宏观性质，如电解质的离子电导率和介电常数（第 4 节），并揭示电极-电解质界面反应机制（第 5 节）依次介绍。最后，提供了关于将 MD 模拟应用于液体电解质的当前挑战和未来方向的一般性结论和有见地的观点。重点强调机器学习技术，以解决 MD 模拟和电解质研究面临的这些具有挑战性的问题，并促进下一代可充电电池先进电解质的合理设计。
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