Molecular Modelling of Aqueous Batteries

TL;DR

This paper reviews molecular modelling techniques, especially DFT-based molecular dynamics and AI methods, to understand ion processes and design new aqueous battery systems for safer, sustainable energy storage.

Contribution

It provides an up-to-date overview of molecular modelling applications, including recent AI advancements, in the study and design of aqueous batteries.

Findings

Enhanced understanding of ion solvation and electron transfer processes.

Application of machine learning to accelerate molecular simulations.

Case studies on water-in-salt electrolytes and Zn-ion batteries.

Abstract

Aqueous batteries play an increasingly important role for the development of sustainable and safety-prioritised energy storage solutions. Compared to conventional lithium-ion batteries, the cell chemistry in aqueous batteries share many common features with those of electrolyzer and pseudo-capacitor systems because of the involvement of aqueous electrolyte and proton activity. This imposes the needs for a better understanding of the corresponding ion solvation, intercalation and electron transfer processes at atomistic scale. Therefore, this chapter provides an up-to-date overview of molecular modelling techniques and their applications in aqueous batteries. In particular, we emphasize on the dynamical and reactive description of aqueous battery systems brought in by density functional theory-based molecular dynamics simulation (DFTMD) and its machine-learning (ML) accelerated counterpart. Moreover, we also cover the recent advancement of generative artificial intelligence (AI) in molecular and materials design of aqueous batteries. Case studies presented here include popular aqueous battery systems, such as water-in-salt electrolytes, proton-coupled cathode materials, Zn-ion batteries as well as organic redox flow batteries.