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Random-batch list algorithm for short-range molecular dynamics simulations.

Jiuyang Liang¹, Zhenli Xu¹, Yue Zhao¹

¹School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai 200240, China.

The Journal of Chemical Physics

|August 3, 2021

Summary

This summary is machine-generated.

We introduce a fast random-batch list method to accelerate molecular dynamics simulations. This approach significantly speeds up calculations for short-range interactions without compromising accuracy.

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Area of Science:

Computational Physics
Materials Science
Chemical Physics

Background:

Molecular dynamics (MD) simulations are crucial for understanding material properties at the atomic level.
Calculating short-range interactions efficiently is a key challenge in large-scale MD simulations.
Classical neighbor-list methods, like Verlet lists, can be computationally expensive to construct.

Purpose of the Study:

To develop a novel, fast method for calculating short-range interactions in MD simulations.
To reduce the computational cost associated with neighbor searching and interaction calculations.
To maintain or improve the accuracy of MD simulations while increasing speed.

Main Methods:

Introduced the random-batch list method, a stochastic alternative to traditional neighbor lists.
Implemented a two-level neighbor list system with core and shell regions for particles.
Utilized random batches of interacting particles in the shell region to decrease pair computations.
Provided an error estimation for the developed algorithm.

Main Results:

Demonstrated significant acceleration (several-fold) of MD simulations for Lennard-Jones fluids.
Showcased that the random-batch list method achieves this speedup without loss of accuracy.
Confirmed the method's simplicity of implementation and compatibility with linked-cell techniques.

Conclusions:

The random-batch list method offers a substantial speedup for large-scale molecular dynamics simulations.
This approach is versatile, easily integrated with other methods, and extensible to various interaction types.
The method holds promise for advancing the efficiency and scalability of computational materials science and physics research.