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Error estimation on the box size correction formula of diffusion coefficient in molecular simulations.
Bisma Akram1,2, Xiangfei Ji3, Yimeng Zhang1,2
1School of Semiconductor and Physics, North University of China, Taiyuan, 030051, China.
A new formula improves diffusion coefficient corrections in molecular simulations, offering better accuracy than the Yeh-Hummer method for extrapolating small simulation box sizes to real systems.
Area of Science:
- Computational Physics
- Materials Science
- Chemical Engineering
Background:
- Correcting diffusion coefficients for simulation box size is crucial for accurate extrapolation in molecular simulations.
- The Yeh-Hummer formula, a common fluid dynamics-based correction, presents application challenges and uncertainties.
- Review of error issues in diffusion coefficient calculations highlights the need for improved correction methods.
Purpose of the Study:
- To address uncertainties in applying the Yeh-Hummer correction formula for diffusion coefficients.
- To propose a novel correction formula based on mathematical statistics for improved simulation accuracy.
- To evaluate the performance of the new formula against the Yeh-Hummer method using simulation data.
Main Methods:
- Molecular dynamics simulations were performed using the LAMMPS package in the NVT ensemble.
- The Lennard-Jones 12-6 potential model was employed with specific parameters for reduced units.
- Extensive simulation steps were conducted to ensure system equilibration and accurate trajectory data collection.
Main Results:
- The study investigated the dependence of the diffusion coefficient on particle number in Lennard-Jones fluids.
- A new correction formula was developed, converging faster with increasing particle numbers.
- The proposed formula demonstrated slightly better agreement with simulation results compared to the Yeh-Hummer formula.
Conclusions:
- The new correction formula offers a more reliable approach for adjusting diffusion coefficients based on simulation box size.
- This work provides a statistically grounded alternative to existing methods, enhancing the predictive power of molecular simulations.
- Further validation and application of the new formula are recommended for broader use in computational studies.
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