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VENUSpy: A Chemical Dynamics Simulation Program in the Era of Machine Learning and Exascale Computing.

Kazuumi Fujioka1, Ryan Richard2, Jonathan Waldrop2

  • 1Department of Chemistry, University of Hawaii, Honolulu, Hawaii 96822, United States.

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|October 30, 2025
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Summary
This summary is machine-generated.

VENUSpy enhances chemical reaction dynamics simulations by enabling machine learning potentials and exascale computing. This Python tool facilitates hybrid dynamics, overcoming limitations of traditional ab initio methods for complex systems.

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Area of Science:

  • Computational Chemistry
  • Chemical Physics
  • Materials Science

Background:

  • Traditional ab initio molecular dynamics (AIMD) provides high accuracy but is computationally expensive, limiting simulations to small systems and short timescales.
  • Advances in machine learning (ML) potentials and exascale computing offer new avenues for developing potential energy surfaces (PESs).
  • These advancements enable simulations of chemical reaction dynamics for larger systems and longer durations.

Purpose of the Study:

  • Introduce VENUSpy, a Python-based reimplementation and extension of the VENUS code.
  • Facilitate the integration of ML potentials and exascale-ready quantum chemistry packages like NWChemEx.
  • Enable advanced ML/ab initio hybrid dynamics simulations for complex reactive systems.

Main Methods:

  • Developed VENUSpy as a Python framework, extending the classical VENUS code.
  • Demonstrated interfacing capabilities with NWChemEx's top-level classes and objects for reaction dynamics.
  • Integrated modern Python tools to enhance the original code's versatility, including initial sampling and trajectory propagation.
  • Enabled ML/ab initio hybrid dynamics simulations.

Main Results:

  • VENUSpy successfully interfaces with ML potentials and the developing NWChemEx package.
  • The framework preserves the core functionalities of the original VENUS code while adding modern Python integration.
  • Demonstrated the capability for ML/ab initio hybrid dynamics simulations, offering advantages over standalone AIMD and MLMD.
  • Facilitated rapid development of new methodologies for studying complex reactive systems.

Conclusions:

  • VENUSpy provides a versatile, modular framework for advanced chemical reaction dynamics simulations.
  • It bridges the gap between high-accuracy AIMD and efficient ML-based methods.
  • The tool accelerates research in complex reactive systems by enabling hybrid simulation approaches.