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Stabilizing Anion-Derived Interphase by Machine-Learning-Accelerated Screening of Out-of-Shell Co-Solvents for
Yaxin Ru1, Feng Wang1,2, Xiaoyu Yu1
1State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), Department of Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen361005, P. R. China.
An out-of-shell co-solvent strategy enhances aqueous zinc battery stability by promoting anion-derived SEI formation and suppressing hydrogen evolution. Machine learning accelerates electrolyte screening, improving battery performance and longevity.
Area of Science:
- Electrochemistry
- Materials Science
- Computational Chemistry
Background:
- Aqueous zinc batteries (AZBs) struggle with unstable Zn anode SEI and HER competition.
- Conventional co-solvents create a trade-off between HER suppression and SEI formation.
Purpose of the Study:
- To develop an out-of-shell co-solvent strategy for stable AZBs.
- To identify an optimal co-solvent using machine learning molecular dynamics (MLMD).
Main Methods:
- Machine learning molecular dynamics (MLMD) for accelerated screening of 28 co-solvent candidates.
- In situ spectroscopic characterization to analyze solvation environment.
- Electrochemical testing of Zn∥Cu and Zn∥I2 cells.
Main Results:
- N,N-dimethylacetamide (DMAC) identified as an optimal co-solvent.
- DMAC reconstructs solvation, facilitating desolvation and anion-derived SEI formation.
- DMAC electrolyte shows high Coulombic efficiency (99.3% over 950 cycles) and stability (12,000 cycles).
Conclusions:
- The out-of-shell co-solvent strategy effectively suppresses HER and promotes stable SEI formation in AZBs.
- MLMD accelerates electrolyte discovery and design.
- A feedback loop integrating dynamic interfacial processes enhances MLMD accuracy for performance regulation.
