ArticleACS nano2024
Effect of Interlayer Bonding on Superlubric Sliding of Graphene Contacts: A Machine-Learning Potential Study.
Article in ACS nano, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Unveiling surface and subsurface atomic vacancies in MoSNature communications · 2026Article
- Electrochemically Engineered GQDs for Selective Tumor Targeting and Oxidative Nanotherapy in Triple-Negative Breast Cancer.ACS omega · 2026Article
- A Theoretical Study on Friction of Macroscale Patterned Surfaces: Implications for Scaling Up Superlubricity.ACS applied materials & interfaces · 2025Article
- Frictional Dissipation and Scaling Laws at van der Waals Interfaces: The Role of Edge and Corner Elastic Moiré Pinning.ACS nano · 2025Article
- Can Neural Networks Learn Atomic Stick-Slip Friction?ACS applied materials & interfaces · 2025Article
- Chemifriction and Superlubricity: Friends or Foes?The journal of physical chemistry letters · 2025Article
- Superlubric Graphullerene.Nano letters · 2024Article
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Authors and funding
4 authors.
Funding
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Abstract
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.
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