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Theoretical study of large-scale graphene on the Cu(111) surface using machine learning potential.
Jingli Han1,2, Rubén Cabello2, Jordi Bonet Ruiz2
1School of Material and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450001, China.
Physical Chemistry Chemical Physics : PCCP
|August 8, 2025
Summary
This study explores large-scale graphene interactions with copper surfaces, revealing how size impacts graphene
Area of Science:
- Materials Science
- Surface Science
- Computational Chemistry
Background:
- Graphene properties on metal surfaces are size-dependent.
- High-precision studies of larger graphene structures are limited.
Purpose of the Study:
- Investigate large-scale graphene interaction with Cu(111).
- Determine average adsorption and formation energies for various graphene configurations.
- Establish relationships between energy and carbon atom number.
Main Methods:
- Derived force field parameters using high-dimensional neural network potential for the graphene-Cu(111) system.
- Validated the applicability and accuracy of the derived parameters.
- Evaluated energies for graphene nanosheets and nanoribbons (zigzag and armchair).
Main Results:
- Quantified average adsorption and formation energies for different graphene sizes and shapes.
- Established correlations between these energies and the number of carbon atoms.
- Demonstrated the accuracy of the high-dimensional neural network potential for this system.
Conclusions:
- Graphene's structural evolution and stability on Cu(111) are size-dependent.
- Adsorption behavior varies with graphene configuration and size.
- Provides insights into nanoscale to mesoscale graphene-Cu(111) interactions.
