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Kinetic Monte Carlo simulations with minimal searching
1Department of Mathematics, University of Tennessee, Knoxville, Tennessee 37996-1300, USA. schulze@math.utk.edu
Summary
This study introduces a faster algorithm for Kinetic Monte Carlo (KMC) simulations used in epitaxial crystal growth. The new method significantly reduces computation time by optimizing rate searching, improving simulation efficiency.
Area of Science:
- Materials Science
- Computational Physics
- Chemical Engineering
Background:
- Kinetic Monte Carlo (KMC) simulations are crucial for modeling epitaxial crystal growth.
- Current KMC methods often rely on binary trees for rate searching, with a time complexity of O(log(2) M).
Purpose of the Study:
- To develop a more efficient algorithm for KMC simulations in epitaxial crystal growth.
- To reduce the computational time required for KMC simulations.
Main Methods:
- Developed a list-based algorithm tailored for KMC simulations with a finite set of distinct rates.
- Compared the performance of the new algorithm against existing binary tree-based methods.
Main Results:
- The new algorithm achieves computation times largely independent of the number of rates (M).
- Achieved typical reductions in computation time of 30% to 50% for standard simulations.
- Observed increased efficiency gains for larger-scale simulations.
Conclusions:
- The developed list-based algorithm offers a significant speedup for KMC simulations in epitaxial crystal growth.
- This optimization is particularly beneficial for simulations involving a finite number of distinct rates.
- The findings pave the way for more extensive and efficient atomistic simulations of crystal growth.