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

The field of chemical reaction dynamics has evolved considerably since its inception, driven by advances in computational power and theoretical methodologies. While ab initio molecular dynamics (AIMD) simulations offer high accuracy by computing forces directly from electronic structure theory, its high computational cost limits its applicability to small systems and short time scales. Machine learning (ML) potentials and exascale computing offer new ways of developing potential energy surfaces (PESs) for chemical reaction dynamics simulations of longer times scales and larger systems. In this work, we introduce VENUSpy, a Python-based reimplementation and extension of the classical VENUS code, designed to interface with existing ML-based potentials and the exascale-ready quantum chemistry package NWChemEx. While NWChemEx is in development, VENUSpy demonstrates interfacing with its top-level classes and objects for use in reaction dynamics simulations. VENUSpy preserves the large library of initial sampling and classical trajectory propagation of the original, Fortran-based VENUS, while expanding its versatility through integration with modern Python tools. This modular framework enables ML/ab initio hybrid dynamics simulations, which offers distinct advantages to AIMD simulations and MLMD simulations, as well as fosters rapid development of new methodologies for studying complex reactive systems.