System-Bath Modeling in Vibrational Spectroscopy via Molecular Dynamics: A Machine Learning Framework for Hierarchical Equations of Motion (HEOM)

TL;DR

This paper introduces a machine learning framework that constructs system-bath models from molecular dynamics data, enabling quantum simulations of vibrational energy relaxation and dephasing in solutions, validated by infrared spectra.

Contribution

It develops a novel machine learning approach to create system-bath models compatible with HEOM, capturing anharmonicity and non-Markovian effects from classical MD data.

Findings

Model accurately reproduces infrared spectra.

Combining Brownian oscillator and Drude SDFs improves performance.

Enables quantum treatment of ultrafast vibrational dynamics.

Abstract

Molecular vibrations in solutions, especially OH stretching and bending in water, drive ultrafast energy relaxation and dephasing in chemical and biological systems. We present a machine learning approach for constructing system-bath models of intramolecular vibrations in solution, compatible with quantum simulations via the hierarchical equations of motion (HEOM). Using classical molecular dynamics trajectories generated with a force field specifically developed for quantum molecular dynamics, the model captures anharmonic mode coupling and non-Markovian dissipation through spectral distribution functions (SDFs). These features, in turn, enable quantum mechanical treatment of ultrafast energy relaxation, vibrational dephasing, and thermal excitation within the HEOM framework.. The trained model yields physically interpretable parameters, validated against infrared spectra. Notably, combining Brownian oscillator and Drude SDFs -- representing inter- and intramolecular vibrational modes -- significantly improves learning performance and supports rigorous simulation of nonlinear vibrational spectroscopy.