Modeling Intermolecular and Intramolecular Modes of Liquid Water Using Multiple Heat Baths: Machine Learning Approach

Area of Science:

• Computational Chemistry
• Quantum Simulation
• Spectroscopy

Background:

• Molecular vibrations in dissipative environments involve complex intermolecular and intramolecular modes.
• These collective modes pose challenges for accurate quantum simulations of chemical and biological processes.

Purpose of the Study:

• To introduce a simplified system-bath model for describing complex molecular motion in dissipative environments.
• To develop a machine-learning approach for determining system parameters and system-bath coupling spectral distributions.

Main Methods:

• Utilized a system-bath model where intramolecular modes are the system and environmental degrees of freedom form the heat bath.
• Employed machine learning on molecular dynamics (MD) trajectories to derive system parameters and spectral distribution functions.
• Applied the method to liquid water using SPC/E and POLI2VS models for vibrational spectroscopy.

Main Results:

• Successfully determined system parameters for symmetric-stretch, asymmetric-stretch, and bend modes.
• Characterized bath spectral distribution functions representing intramolecular-intermolecular mode interactions.
• Elucidated nonintuitive energy relaxation pathways between intramolecular and intermolecular modes in liquid water.

Conclusions:

• The developed machine-learning system-bath model effectively simplifies and enhances quantum simulations of molecular vibrations.
• This approach provides valuable insights into energy transfer dynamics in complex molecular systems like liquid water.