Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids.

Area of Science:

• Materials Science
• Computational Chemistry
• Electrochemistry

Background:

• Solvate ionic liquids (SILs) show potential as electrolyte materials for batteries.
• Current SILs often use simple glyme ligands, limiting design possibilities.
• A broader range of ether ligands could improve SIL stability and performance.

Purpose of the Study:

• To computationally screen complex ether ligands for enhanced solvate ionic liquid stabilization.
• To develop a fast and accurate computational method for identifying optimal ligand-ion interactions.
• To discover new ether ligands for lithium, magnesium, and sodium-ion batteries.

Main Methods:

• Density functional theory (DFT) calculations to generate training data.
• Active learning to train a neural network interatomic potential.
• Computational screening of candidate ether ligands for Li+, Mg2+, and Na+ SILs.
• Analysis of ligand-ion binding affinity and electrochemical stability.

Main Results:

• A transferable and efficient neural network potential was developed.
• Candidate ether ligands with superior binding affinity and electrochemical stability were identified compared to traditional glymes.
• The study established structure-property relationships based on coordination sphere geometry.

Conclusions:

• Complex ether ligands offer a promising route to designing highly stable solvate ionic liquids.
• The computational approach enables rapid discovery of advanced electrolyte materials.
• This work paves the way for next-generation batteries with improved electrolytes.