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GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic

Zheyong Fan¹, Yanzhou Wang², Penghua Ying³

¹College of Physical Science and Technology, Bohai University, Jinzhou 121013, People's Republic of China.

The Journal of Chemical Physics

|September 22, 2022

Summary

This summary is machine-generated.

We enhanced machine-learned potentials (MLPs) using the neuroevolution potential (NEP) framework for accurate, efficient atomistic simulations. The gpumd package and new Python tools facilitate large-scale modeling and active learning for materials discovery.

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Area of Science:

Materials Science
Computational Chemistry
Physics

Background:

Machine-learned potentials (MLPs) are crucial for atomistic simulations.
Existing methods face challenges in accuracy and computational efficiency.

Purpose of the Study:

To advance the neuroevolution potential (NEP) framework for enhanced MLP accuracy and efficiency.
To introduce the gpumd package for large-scale atomistic simulations.
To develop an active-learning scheme for efficient MLP training.

Main Methods:

Improved atomic-environment descriptors using Chebyshev basis functions and multi-body angular contributions.
Efficient implementation of the NEP approach on graphics processing units (GPUs).
Development of a workflow for NEP model construction and an active-learning scheme.

Main Results:

Achieved above-average accuracy and superior computational efficiency compared to state-of-the-art MLPs.
Demonstrated successful application in large-scale atomistic simulations.
Introduced Python packages (gpyumd, calorine, pynep) for seamless integration.

Conclusions:

The gpumd package offers a powerful tool for high-accuracy, large-scale atomistic simulations.
The NEP approach with active learning enables efficient MLP construction from minimal data.
The developed tools facilitate advanced materials modeling and discovery.