Simulations of dielectric permittivity of water by Machine Learned Potentials with long-range Coulombic interactions

TL;DR

This paper introduces a machine learning framework that accurately predicts the dielectric permittivity of water by incorporating long-range electrostatics and various boundary conditions, advancing the modeling of polar liquids.

Contribution

It presents a novel unified ML approach that includes long-range Coulomb interactions and different boundary conditions to compute dielectric properties of water.

Findings

Consistent computation of dielectric permittivity under multiple boundary conditions.

Demonstration of the importance of long-range electrostatics in dielectric response.

Development of a generalizable ML framework for polar liquids.

Abstract

The dielectric permittivity of liquid water is a fundamental property that underlies its distinctive behaviors in numerious physical, biological, and chemical processes. Within a machine learning framework, we present a unified approach to compute the dielectric permittivity of water, systematically incorporating various electric boundary conditions. Our method employs a long-range-inclusive deep potential trained on data from hybrid density functional theory calculations. Dielectric response is evaluated using an auxiliary deep neural network that predicts the centers of maximally localized Wannier functions. We investigate three types of electric boundary conditions--metallic, insulating, and Kirkwood-Frohlich--to assess their influence on correlated dipole fluctuations and dielectric relaxation dynamics. In particular, we demonstrate a consistent methodology for computing the Kirkwood correlation factor, correlation length, and dielectric permittivity under each boundary condition, where long-range electrostatics play a critical role. This work establishes a robust and generalizable machine-learning framework for modeling the dielectric properties of polar liquids under diverse electrostatic environments.