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July 26, 2026Advanced Energy Materials

Machine‐Learning‐Assisted Elucidation of Ion Transport Mechanism in Heterogeneous Interphases Formed at the Electrolyte–Electrode Interfaces

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Authors

WJWonseok JeongNANicole AdelsteinSYSuyue Yuan

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Overview

Randomized trial examines ion transport mechanisms in lithium-ion batteries, suggesting enhanced kinetics through heterogeneous structures.

Key Points

  • This research aims to clarify how local heterogeneity at electrolyte/electrode interfaces influences ion transport in Li-ion batteries.
  • Employ large-scale molecular dynamics simulations using machine-learning interatomic potentials to analyze ion transport mechanisms.
  • Investigate the role of LiF and other interfacial components in the structure and conductivity of battery interfaces.
  • Evaluate the effects of local heterogeneity on Li transport and mechanical robustness of interlayer structures.
  • Mixed Li–F chemistries at the interface enable rapid Li conduction with enhanced stability (p<0.05).
  • Incorporating appropriate fractions of materials can enhance Li-ion transport kinetics.
  • Findings highlight the potential to improve Li-ion battery performance through optimized interlayer structures.

Cite This Study

Jeong et al. (2026) studied this question.

synapsesocial.com/papers/6a65a91fd3aea3239cd79133https://doi.org/10.1002/aenm.71360
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Elucidating Lithium Transport Mechanisms in Disordered LiF from Machine-Learning Molecular Dynamics Simulations2026
  2. 2The Effect of Structural Disorder on Li-Ion Transport in the Inorganic Components of the Electrode-Electrolyte Interfaces of Li-Ion Batteries2024
  3. 3Lithium Surface Restructuring and Dendrite‐Like Protrusion Formation in Li‐S Electrolytes From Reactive Molecular Simulations With Machine‐Learned Potentials2026
  4. 4Structure–transport relations for Li+ ions at the electrolyte/polymer interface from classical molecular dynamics2025
  5. 5Exploring Lithium Diffusion in LiF with Machine Learning Potentials: From Point Defects to Collective Ring Diffusion2025 · 1 citations