Polarizable Water Model with Ab Initio Neural Network Dynamic Charges and Spontaneous Charge Transfer
Qiujiang Liang, Jun Yang

TL;DR
This paper introduces a novel polarizable water model that combines ab initio neural network predicted charges with explicit charge transfer, enabling highly accurate simulations of water's properties across phases.
Contribution
The study develops a transferable neural network-based charge model integrated with quantum mechanical data to accurately simulate polarization and charge transfer in water.
Findings
Successfully reproduces water's spectral features and phase properties.
Captures interfacial electric fields and hydrogen-bond dynamics.
Enables large-scale, high-accuracy molecular simulations.
Abstract
Simulating water accurately has been a challenge due to the complexity of describing polarization and intermolecular charge transfer. Quantum mechanical (QM) electronic structures provide an accurate description of polarization in response to local environments, which is nevertheless too expensive for large water systems. In this study, we have developed a polarizable water model integrating Charge Model 5 atomic charges at the level of second-order M{\o}ller-Plesset perturbation theory, predicted by an accurate and transferable Charge Neural Network (ChargeNN) model. The spontaneous intermolecular charge transfer has been explicitly accounted for, enabling a precise treatment of hydrogen bonds and out-of-plane polarization. Our ChargeNN water model successfully reproduces various properties of water in gas, liquid and solid phases. For example, ChargeNN correctly captures the…
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Taxonomy
TopicsSpectroscopy and Quantum Chemical Studies · Neural Networks and Reservoir Computing · Earthquake Detection and Analysis
