Probing the Temporal Response of Liquid Water to a THz Pump Pulse Using Machine Learning-Accelerated Non-Equilibrium Molecular Dynamics
Kit Joll, Philipp Schienbein

TL;DR
This study introduces a machine learning-accelerated non-equilibrium molecular dynamics method to simulate and analyze the ultrafast response of liquid water to a THz pump pulse, capturing key spectroscopic and dynamical features.
Contribution
The paper presents a novel machine learning potential that incorporates time-dependent electric fields, enabling accurate, long-timescale simulations of non-equilibrium water dynamics at ab initio quality.
Findings
Reproduces experimental transient birefringence and relaxation times.
Identifies energy transfer pathways from librational modes to other vibrations.
Detects a 0.7 ps timescale for librational energy dissipation.
Abstract
Ultrafast, time-resolved spectroscopies enable the direct observation of non-equilibrium processes in condensed-phase systems and have revealed key insights into energy transport, hydrogen-bond dynamics, and vibrational coupling. While ab initio molecular dynamics (AIMD) provides accurate, atomistic resolution of such dynamics, it becomes prohibitively expensive for non-equilibrium processes that require many independent trajectories to capture the stochastic nature of excitation and relaxation. To address this, we implemented a machine learning potential that incorporates time-dependent electric fields in a perturbative fashion, retaining AIMD-level accuracy. Using this approach, we simulate the time-dependent response of liquid water to a 12.3 THz Gaussian pump pulse (1.3 ps width), generating 32 ns of total trajectory data. With access to ab initio-quality electronic structure, we…
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