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Surface growth on percolation networks by a conserved-noise restricted solid-on-solid growth model.
1Department of Physics and Department of Nano-Science & Technology of Graduate School, Kyungpook National University, Daegu 41566, Korea.
Physical Review. E
|March 18, 2016
Summary
Surface growth models were studied on diluted lattices, specifically percolation networks. Results on critical percolation networks showed deviations from theoretical predictions, unlike those on deterministic fractal substrates.
Area of Science:
- Condensed Matter Physics
- Statistical Mechanics
- Materials Science
Background:
- Surface growth phenomena are crucial in various scientific fields.
- Understanding growth dynamics on complex, disordered substrates is a key challenge.
- Fractal lattices and percolation networks represent complex substrate geometries.
Purpose of the Study:
- To investigate surface growth using the conserved-noise restricted solid-on-solid model on diluted lattices.
- To measure growth and roughness exponents on infinite and backbone percolation networks.
- To compare experimental results with theoretical predictions from the fractional Langevin equation.
Main Methods:
- Utilized the conserved-noise restricted solid-on-solid model for surface growth simulations.
- Employed Monte Carlo simulations on two-dimensional percolation networks.
- Calculated the growth exponent (β) and roughness exponent (α) based on surface width scaling.
Main Results:
- Excellent agreement was found between simulation results and theoretical predictions on deterministic fractal substrates.
- Significant deviations (8%-12%) were observed between simulation results and theoretical predictions on critical percolation networks.
- The study highlights the impact of lattice disorder on surface growth dynamics.
Conclusions:
- The conserved-noise restricted solid-on-solid model exhibits different scaling behaviors on deterministic versus disordered fractal substrates.
- Critical percolation networks introduce deviations from fractional Langevin equation predictions for surface growth.
- Further theoretical and computational work is needed to fully understand growth dynamics on complex disordered systems.
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