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

TL;DR

GPUMD is an open-source package that advances machine-learned potentials by improving accuracy and efficiency in large-scale atomistic simulations, integrating new descriptors, GPU acceleration, and active learning.

Contribution

The paper introduces enhanced NEP models with improved descriptors, GPU implementation, and an active-learning scheme, making GPUMD a highly accurate and efficient tool for atomistic simulations.

Findings

NEP models outperform state-of-the-art MLPs in accuracy and efficiency

GPU implementation significantly accelerates simulations

Active learning reduces training data requirements

Abstract

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package GPUMD. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models, and we demonstrate their application in large-scale atomistic simulations. By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient. These results demonstrate that the GPUMD package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations. To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model. Finally, we introduce three separate Python packages, GPYUMD, CALORINE, and PYNEP, which enable the integration of GPUMD into Python workflows.