AI-Guided Design of Ternary Eutectic Electrolytes: Dual Regulation of Zn²⁺ Solvation and Hydrogen-Bond Network for Interfacial Stability and Low Temperature Zn-Ion Batteries

Abstract

Aqueous Zn-ion batteries (AZIBs) are emerging as a promising solution for large-scale grid energy storage due to their low manufacturing cost, intrinsic safety, recyclability, and minimal environmental impact, positioning them as viable alternatives to flammable organic-electrolyte-based batteries. However, challenges such as water-triggered parasitic reactions, dendrite growth, and limited low- temperature performance hinder commercialization. Electrolyte engineering, particularly through additive-based approaches, has shown promise in addressing these challenges by modifying Zn2+ solvation structures to suppress parasitic reactions and stabilize the Zn anode during cycling. High donor number (DN) additives have been recognized for their effectiveness in enhancing cell performance, yet their identification relies on trial-and-error processes, owing to the uncharacterized properties of many candidate materials. In this study, a deep learning model is employed to predict DN-related properties for over 1,000,000 samples. By systematically screening materials based on structural and functional criteria, the study identifies N,N-dimethylmethanesulfonamide (DMMSA) as an advanced material. Using DMMSA, a ternary eutectic electrolyte (TEE) composed of hydrated Zn(ClO4)2·6H2O, DMMSA, and H2O is developed as a next-generation alternative to conventional aqueous electrolytes. Experimental and theoretical analyses confirm that DMMSA regulates the Zn2+ solvation structure and reorganizes the hydrogen bond network, effectively inhibiting parasitic reactions and enabling stable operation at low temperatures. The inert methyl groups of DMMSA, attributed to an "end-capping effect", disrupt chain- like hydrogen bonds between H2O molecules, thereby improving ionic conductivity and enhancing low- temperature performance. This electrolyte achieves a remarkable cycle life of 4,842 hours of cycling at 0.5 mA cm-2 and an areal capacity of 0.5 mAh cm-2 in Zn//Zn cells. By integrating AI-assisted material discovery with experimental and theoretical validation, our approach offers a pathway to accelerate advancements in battery technology and foster innovative solutions for the industry’s future.