sbml4md: A computational platform for System-Bath Modeling via Molecular Dynamics powered by Machine Learning

TL;DR

sbml4md is a novel computational platform that uses machine learning to extract parameters from molecular dynamics data, enabling accurate and efficient simulation of nonlinear vibrational spectra in molecular liquids without empirical fitting.

Contribution

The paper introduces sbml4md, a new algorithm and software that leverages machine learning to extract parameters for multimode anharmonic Brownian models from MD trajectories, enhancing spectral simulations.

Findings

Enables parameter extraction for nonlinear vibrational spectra

Integrates classical MD with HEOM framework for accurate modeling

Provides a scalable, minimally empirical simulation approach

Abstract

We introduce sbml4md, a newly developed algorithm implemented as a software package to extract parameters of multimode anharmonic Brownian (MAB) 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 [Park, Jo, and 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 supporting material.