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

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 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.

Significance of Antisolvents on Solvation Structures Enhancing Interfacial Chemistry in Localized High-Concentration Electrolytes

“…In other words, the weakened Li + –FSI – association is more likely to promote participation of DMC solvents in the Li + solvation sheath, which is also consistent with the FTIR spectra. For desolvation energy, it is important for the desolvation process, and the weaker ion desolvation energy is beneficial for ion diffusion between the electrolyte/electrode interface and improves the rate performance of batteries. − In addition, the positive correlation between desolvation energy and binding energy has been demonstrated by many works. − Therefore, the reduced binding energy of the Li + ···DMC and Li + ···FSI – interactions reveals that the Li + desolvation energy of the solvated structure is lower in LHCEs. Hence, the LHCEs can effectively improve the rate performance of LIBs.…”

Unity of Opposites between Soluble and Insoluble Lithium Polysulfides in Lithium–Sulfur Batteries

“…They can reflect the dissociation degrees and solvation states of intermediates in the electrolyte, thus providing robust evidence for determining the states of LPSs in the battery during cycling. [89,107] In particular, ab initio molecular dynamics (AIMD) allows chemical bond breaking and forming events to occur, [108] which can help reveal the structures and charge evolutions of intermediates during charge/discharge. These characteristics have also demonstrated great ability in elucidating the effect of RM on the reaction pathway of SRR.…”

MetalWalls: Simulating electrochemical interfaces between polarizable electrolytes and metallic electrodes

“…For the minimization of quadratic functions of the form U = 1 2 xAx + bx + c -which corresponds to finding the solution of the linear system of equations Ax + b = 0 -preconditioning the symmetric, positive-definite matrix A has proven to be an effective tool for improving the speed of convergence of the conjugate gradient algorithm. Indeed, the convergence of the algorithm depends mainly on the condition number of the matrix A (see Ref.…”