Effect of Interlayer Bonding on Superlubric Sliding of Graphene Contacts: A Machine-Learning Potential Study

Penghua Ying, Amir Natan, Oded Hod, Michael Urbakh

Research output: Contribution to journalArticlepeer-review


Surface defects and their mutual interactions are anticipated to affect the superlubric sliding of incommensurate layered material interfaces. Atomistic understanding of this phenomenon is limited due to the high computational cost of ab initio simulations and the absence of reliable classical force-fields for molecular dynamics simulations of defected systems. To address this, we present a machine-learning potential (MLP) for bilayer defected graphene, utilizing state-of-the-art graph neural networks trained against many-body dispersion corrected density functional theory calculations under iterative configuration space exploration. The developed MLP is utilized to study the impact of interlayer bonding on the friction of bilayer defected graphene interfaces. While a mild effect on the sliding dynamics of aligned graphene interfaces is observed, the friction coefficients of incommensurate graphene interfaces are found to significantly increase due to interlayer bonding, nearly pushing the system out of the superlubric regime. The methodology utilized herein is of general nature and can be adapted to describe other homogeneous and heterogeneous defected layered material interfaces.

Original languageEnglish
Pages (from-to)10133-10141
Number of pages9
JournalACS Nano
Issue number14
StatePublished - 9 Apr 2024


  • Atomic defects
  • Graphene interfaces
  • Interlayer bonding
  • Machine-learning potentials
  • Molecular dynamics
  • Nanoscale friction
  • Structural superlubricity

All Science Journal Classification (ASJC) codes

  • General Materials Science
  • General Engineering
  • General Physics and Astronomy


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