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Hybrid Machine Learning-Enabled Potential Energy Model for Atomistic Simulation of Lithium Intercalation into

Po-Yu Yang1, Yu-Hsuan Chiang2, Chun-Wei Pao1,3

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Journal of Chemical Theory and Computation
|May 4, 2023
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Summary

Researchers developed a hybrid machine learning model to simulate lithium intercalation in graphite for advanced lithium-ion batteries (LIBs). This model reveals lithium plating mechanisms and discovers new dense graphite intercalation compounds (GICs) for higher energy density and charging rates.

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Area of Science:

  • Materials Science
  • Electrochemistry
  • Computational Materials Science

Background:

  • Graphite is a primary anode material for lithium-ion batteries (LIBs).
  • Increasing demand for higher energy density and faster charging rates necessitates deeper understanding of lithium intercalation and plating in graphite.
  • Existing models require enhancement for comprehensive simulation of these processes.

Purpose of the Study:

  • To develop a hybrid machine learning potential energy model for simulating lithium intercalation in graphite across a wide range of conditions.
  • To elucidate the mechanisms behind lithium plating and diffusion in graphite electrodes.
  • To discover novel, dense graphite intercalation compounds (GICs) for improved LIB performance.

Main Methods:

  • Trained a hybrid machine learning potential energy model using dihedral-angle-corrected registry-dependent potential (DRIP), Ziegler-Biersack-Littmark (ZBL), and spectral neighbor analysis (SNAP) potentials.
  • Performed atomistic simulations to investigate lithium intercalation from plating to overlithiation.
  • Analyzed lithium atom trapping near graphite edges and identified stable GIC structures.

Main Results:

  • Identified high hopping barriers near graphite edges as the cause of intercalated lithium atom trapping and subsequent lithium plating.
  • Discovered a stable, dense graphite intercalation compound (GIC) LiC4 with a theoretical capacity of 558 mAh/g.
  • Observed retention of the LiC4 structure's nearest Li-Li distance up to a capacity of 845.2 mAh/g (LiC2.6).

Conclusions:

  • The hybrid machine learning approach enables extensive investigation of lithium intercalation in graphite, revealing key mechanisms.
  • The study demonstrates the potential for discovering new dense GICs for advanced LIBs.
  • Findings pave the way for developing LIBs with enhanced energy density and charging rates.