Applying Classical,  Ab Initio , and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Yao, Nan; Chen, Xiang; Fu, Zhongheng; Zhang, Qiang

doi:10.1021/acs.chemrev.1c00904

reviewChemical ReviewsMay 16, 2022Closed access

Applying Classical, Ab Initio , and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

NYNan Yao XCXiang Chen ZFZhongheng Fu QZQiang Zhang

Tsinghua University

PubMed

Indexed incrossrefpubmed

Abstract

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…

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Topics & keywords

Topics

Keywords

Electrolyte
Ionic liquid
Battery (electricity)
Chemistry
Molecular dynamics
Ionic conductivity
Ab initio
Conductivity

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