Machine-Learning-Assisted Screening of Electrolyte Additives for Aqueous Zinc-Ion Batteries via a Molecular...

Area of Science:

• Electrochemistry
• Materials Science
• Computational Chemistry

Background:

• Additive engineering is crucial for enhancing zinc anode stability in aqueous zinc-ion batteries.
• Traditional trial-and-error methods for additive screening are inefficient and impede commercialization.

Purpose of the Study:

• To develop a descriptor framework-driven strategy integrating machine learning for rapid and precise screening of electrolyte additives.
• To identify key descriptors that influence zinc anode performance and elucidate the underlying mechanisms.

Main Methods:

• Utilized machine learning to identify molecular size/volume and maximum electrostatic potential (ESPmax) as key descriptors for cumulative capacity (CC).
• Validated findings through simulations and experimental dual validation.
• Employed a descriptor fitting and screening approach guided by the developed machine learning model.

Main Results:

• Achieved a high Pearson correlation coefficient of 0.8707 for log(CC) prediction using the identified descriptors.
• Elucidated the mechanism of additive adsorption on the zinc anode, modulating interfacial water molecules and inhibiting hydrogen evolution.
• Identified Potassium L-Aspartate (PL-As) as a high-performance additive.

Conclusions:

• The machine learning-driven descriptor framework enables efficient and accurate screening of additives for aqueous zinc-ion batteries.
• The identified additive, PL-As, significantly improves zinc anode stability, achieving 99.86% coulombic efficiency over 5000 cycles.
• This approach establishes a new paradigm for developing high-stability aqueous electrolyte additives and advancing battery technology.