AI-Driven Electrolyte Additive Selection to Boost Aqueous Zn-Ion Batteries Stability.

Area of Science:

• Materials Science
• Electrochemistry
• Computational Chemistry

Background:

• Aqueous Zn-ion batteries (AZIBs) face stability challenges for large-scale energy storage.
• Current electrolyte additive selection relies on inefficient trial-and-error methods.

Purpose of the Study:

• To develop a data-driven strategy for selecting effective electrolyte additives for AZIBs.
• To identify key molecular descriptors influencing battery stability using machine learning.

Main Methods:

• Utilized theoretically computed surface free energy as a stability descriptor.
• Created a machine learning model trained on a database of calculated additive properties.
• Employed interpretable linear regression and AI clustering for analysis.
• Validated findings through experimental verification of selected additives.

Main Results:

• Identified the number of heavy atoms and liquid surface tension as critical factors for additive performance.
• AI clustering pinpointed optimal regions for additive design.
• 1,2,3-butanetriol and acetone demonstrated exceptional performance in experimental tests.

Conclusions:

• The integrated methodology offers a rational approach to designing electrolyte additives for AZIBs.
• This data-driven strategy overcomes computational challenges with large solvent molecules.
• The findings pave the way for more efficient development of stable AZIBs for energy storage.