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Atomistic Insights into Stress-Driven Lithiation at Silicon Anode Crack Tips

  • Harbin Institute of Technology
  • School of Chemistry and Chemical Engineering, Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

Abstract

Silicon is a leading candidate for next-generation lithium-ion battery anodes due to its high theoretical capacity. However, large volume changes during lithiation and delithiation generate significant mechanical stress, leading to particle fracture and crack formation, which degrade electrode performance. In this work, we investigate the lithiation dynamics at the tip of the crack along the [112̅] direction using molecular dynamics simulations driven by a machine learning potential trained with the NEP framework. By applying a range of tensile strains, we systematically explored how crack-tip stress fields influence the atomic-scale lithiation process. Under zero stress, lithium insertion proceeds uniformly, generating a flat amorphous–crystalline interface. Moderate tensile stress leads to the formation of a stepped interface morphology, which is consistent with a ledge-mediated amorphization mechanism. Quantitative kinetic analysis reveals that tensile strain reduces the activation energy for lithiation, thereby accelerating interface propagation. At higher stress levels, the lithiation front becomes unstable and advances via narrow, stress-guided channels that penetrate deeply into the crystalline silicon. These results demonstrate that mechanical stress fields not only modulate lithiation kinetics but also dictate the morphological evolution of the reaction front. This study provides fundamental atomistic insights into the chemo-mechanical coupling that governs lithiation behavior in silicon and may inform the design of more durable high-capacity battery electrodes.

Original languageEnglish
Pages (from-to)58080-58093
Number of pages14
JournalACS Applied Materials and Interfaces
Volume17
Issue number42
DOIs
StatePublished - 22 Oct 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • computational modeling
  • lithiation behavior
  • machine learning potential
  • molecular simulation
  • silicon electrode

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