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GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP

Zihan Yan, Denan Li, Xin Wu, Zhoulin Liu, Chen Hua, Boyi Situ, Hao Yang, Shengjie Tang, Benrui Tang, Ziyang Wang, Shangzhao Yi, Huan Wang, Dian Huang, Ke Li, Qilin Guo, Zherui Chen, Ke Xu, Yanzhou Wang, Ziliang Wang, Gang Tang, Shi Liu, Zheyong Fan, Yizhou Zhu

Abstract

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications.

GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP

Abstract

Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications.
Paper Structure (9 sections, 11 equations, 7 figures)

This paper contains 9 sections, 11 equations, 7 figures.

Table of Contents

  1. Introduction
  2. The GPUMD Package and the NEP method
  3. GPUMDkit Package
  4. Interactive Mode
  5. Command-Line Interface
  6. Case Studies: Phase Transition and Ionic Diffusion of Li$_7$La$_3$Zr$_2$O$_{12}$
  7. Case Studies: Phase Transition and Topological Structure in (Pb,Sr)TiO$_3$
  8. Case Study: Thermal transport of graphene
  9. Conclusions

Figures (7)

  • Figure 1: Schematic diagram of GPUMDkit.
  • Figure 2: (a) Evolution of various terms in the loss function during the NEP training process. (b) Energy, (c) force, and (d) stress values from the NEP model, in comparison to the DFT reference data.
  • Figure 3: Thermodynamic properties of LLZO during heating from 600 to 1200: (a) temperature, (b) pressure, (c) potential and kinetic energy, (d) lattice parameters, (e) volume, and (f) lattice angles.
  • Figure 4: (a) The mean square displacement (MSD) of each atomic species at 800. (b) The evolution of Li-ion diffusivities during the simulation. (c) Arrhenius plot of Li-ion diffusion in t-LLZO and c-LLZO. (d) Density of atomistic states of Li-ions in LLZO. Atomistic energy distribution plot (AEDP) of Li-ions in (e) t-LLZO and (f) c-LLZO.
  • Figure 5: Temperature dependence of structural properties in bulk PbTiO3 and SrTiO3. (a) Lattice constants (left axis) and spontaneous polarization along the polar axis $P_z$ (right axis) of PbTiO3 as a function of temperature, showing the ferroelectric tetragonal ($P4mm$) to paraelectric cubic ($Pm\bar{3}m$) transition at $T_\text{C} \approx \qty{600}{\kelvin}$. (b) Lattice constants (left axis) and the TiO6 octahedral tilt angle (right axis) of SrTiO3, illustrating the antiferrodistortive phase transition at $T_\text{C} \approx \qty{225}{\kelvin}$.
  • ...and 2 more figures