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The authors introduce sbml4md, a software package that extracts parameters for multimode anharmonic Brownian models from MD trajectories using ML to model nonlinear vibrational spectra of molecular liquids. This approach captures vibrational anharmonicity, intermolecular couplings, and bath correlation functions, eliminating the need for empirical fitting and enabling modeling of heterogeneous environments. The extracted parameters are specifically designed for Hierarchical Equations of Motion (HEOM) simulations, allowing for numerically "exact" simulations of nonlinear vibrational spectra with minimal empirical input.
Ditch the empirical fitting: sbml4md uses machine learning to extract parameters from molecular dynamics trajectories for accurate, "exact" simulations of nonlinear vibrational spectra in complex molecular liquids.
We introduce sbml4md, a newly developed algorithm implemented as a software package to extract parameters of multimode anharmonic Brownian models from molecular dynamics (MD) trajectories for simulating nonlinear vibrational spectra of intramolecular modes of molecular liquids. By leveraging machine learning (ML) techniques to capture vibrational anharmonicity, intermolecular couplings, and bath correlation functions for each mode, sbml4md obviates empirical fitting and enables the modeling of environments with spatial and temporal heterogeneity. This work provides a set of parameters specifically tailored for the Hierarchical Equations of Motion (HEOM) framework, enabling numerically "exact" simulations of nonlinear vibrational spectra. Building upon our previous implementation for intramolecular vibrational modes [K. Park, J.-Y. Jo, and Y. Tanimura, J. Chem. Phys. 163, 214104 (2025)], the present code enhances optimization efficiency by explicitly accounting for intermolecular vibrational contributions. This extension enables sbml4md to broaden the applicability of HEOM-based dynamical modeling by seamlessly integrating classical MD approaches, thereby providing a flexible and scalable framework for simulating both linear and nonlinear spectra under realistic conditions with minimal empirical input. The accompanying ML code, written in Python, is provided as the supplementary material.
