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The ionic association is the association of oppositely charged ions in an electrolyte solution to form ion pairs. Bjerrum defined ion pairs as two oppositely charged ions whose electrostatic attraction exceeds the thermal energy of the system, typically expressed as 2kT. Electrostatic attraction depends on ionic charge, separation distance, and the dielectric constant of the medium. Thermal energy, represented by kT, reflects the tendency of ions to move independently due to molecular motion.
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Multilevel Artificial Intelligent Framework Accelerates Electrolytes Design for Aqueous Batteries.

Gaoyang Li1, Xin Liu1,2, Shixiang Ding1

  • 1Laboratory of Advanced Materials, Aqueous Battery Center, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Wusong Laboratory of Materials Science, State Key Laboratory of Porous Materials for Separation and Conversion, College of Smart Materials and Future Energy, Fudan University, Shanghai, P. R. China.

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|April 5, 2026
PubMed
Summary
This summary is machine-generated.

A new multilevel artificial intelligence (AI) framework accelerates the design of aqueous batteries (ABs) by identifying electrolyte additives that enhance electrochemical stability window (ESW) and inhibit hydrogen evolution reactions (HER). This AI approach enables safer, more energetic ABs with extended lifespans.

Keywords:
aqueous batterychemical explainabilityexperimental validationsmachine learning

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Area of Science:

  • Electrochemistry
  • Materials Science
  • Artificial Intelligence

Background:

  • Designing aqueous electrolytes with wide electrochemical stability window (ESW) and resistance to hydrogen evolution reactions (HER) is crucial for safe and energetic aqueous batteries (ABs).
  • Machine learning (ML) faces challenges in electrolyte design due to complex formulations, solvation interactions, and coupled performance metrics, requiring chemically interpretable workflows.

Purpose of the Study:

  • To develop a multilevel artificial intelligence (AI) framework for accelerated electrolyte design in aqueous batteries (ABs).
  • To establish elemental features for wide ESW and identify additives for HER inhibition using ML.
  • To provide chemical explanations for improved performance through unsupervised learning and molecular dynamics simulations.

Main Methods:

  • A multi-task neural network was used to identify features of aqueous electrolytes with wide ESW.
  • A classification-regression model was employed to find additives that inhibit HER.
  • Unsupervised learning and molecular dynamics simulations were combined for chemical interpretation.
  • Experimental validation was performed using symmetric cells and full cells (Zn||VO2).

Main Results:

  • The AI framework successfully identified electrolyte additives with large polar topological structures that reduce HER activity and expand ESW.
  • These additives enhance performance by improving the water confinement effect.
  • Experimental results demonstrated long lifespan (over 1100 h cycling) and reliable operation of a 1.66 Ah device.

Conclusions:

  • The developed multilevel AI framework, integrated with experimental validation, accelerates and rationalizes the design of advanced aqueous batteries (ABs).
  • This approach facilitates the creation of safer, more energetic aqueous batteries with enhanced stability and longevity.