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Observation of dendrite formation at Li metal-electrolyte interface by a machine-learning enhanced constant potential

Taiping Hu1,2, Haichao Huang3, Guobing Zhou1,4

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|August 11, 2025
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Researchers developed a new simulation method to understand lithium dendrite growth in lithium metal batteries. This approach reveals how inhomogeneous lithium deposition initiates dendrite formation, improving battery safety and efficiency.

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Chemistry

Background:

  • Uncontrolled lithium dendrite growth in lithium metal batteries causes low Coulombic efficiency and safety concerns.
  • Understanding dendrite formation is crucial for improving lithium metal battery performance.

Purpose of the Study:

  • To develop and apply a novel simulation method for investigating lithium dendrite nucleation at the atomic scale.
  • To elucidate the mechanism of dendrite formation under constant potential conditions.

Main Methods:

  • Utilized machine learning-accelerated molecular dynamics simulations.
  • Implemented a constant potential approach combining a machine learning force field with the charge equilibration method.
  • Simulated lithium deposition dynamics on lithium metal anode surfaces.

Main Results:

  • Identified inhomogeneous lithium deposition and aggregation within solid electrolyte interphase amorphous inorganic components as key initiators of dendrite nucleation.
  • Provided atomic-scale insights into the dynamic process of dendrite formation.
  • Demonstrated the efficacy of the proposed constant potential simulation method.

Conclusions:

  • The developed simulation method accurately models constant potential conditions for electrochemical interfaces.
  • Microscopic insights into lithium dendrite formation offer pathways for enhancing lithium metal battery performance and safety.
  • The simulation approach has broad potential for modeling complex electrochemical systems.