Performing Path Integral Molecular Dynamics Using an Artificial Intelligence-Enhanced Molecular Simulation Framework.

Journal: Journal of chemical theory and computation
Published Date: Jul 29, 2025

Abstract

This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient path integral molecular dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIMD approaches. By integrating machine learning force fields (MLFFs) into the framework, we rigorously tested its performance through two representative cases: a small-molecule reaction system (double-proton transfer in the formic acid dimer) and a bulk-phase transition system (water-ice phase transformation). Computational results demonstrate that the proposed framework achieves accelerated PIMD simulations while preserving the quantum mechanical accuracy. These findings show that nuclear quantum effects can be captured for complex molecular systems using relatively low computational cost.

Authors

  • Cheng Fan
    Department of Genetics, University of North Carolina, Chapel Hill, North Carolina.
  • Maodong Li
    Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China.
  • Sihao Yuan
    Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
  • Zhaoxin Xie
    Institute of Theoretical and Computational Chemistry, College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China.
  • Dechin Chen
    Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518107, China.
  • Yi Isaac Yang
    Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, 518055 Shenzhen, China.
  • Yi Qin Gao
    Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, China.

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