# Deep neural network for the dielectric response of insulators

## TL;DR

This paper presents a deep neural network model that accurately predicts the dielectric response of insulators by learning from ab-initio data, enabling simulations of complex environments and phase transitions.

## Contribution

The authors develop a symmetry-preserving neural network integrated with the Deep Potential model to predict dielectric responses from electronic structure data.

## Key findings

- Successfully predicts infrared spectra of liquid water and ice under pressure.
- Captures dielectric response in mutating chemical environments.
- Enables simulations of phase transitions beyond direct ab-initio methods.

## Abstract

We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab-initio density functional theory, wherein the electronic centers are uniquely assigned by the maximally localized Wannier functions. When combined with the Deep Potential model of the atomic potential energy surface, the scheme predicts the dielectric response of insulators for trajectories inaccessible to direct ab-initio simulation. The scheme is non-perturbative and can capture the response of a mutating chemical environment. We demonstrate the approach by calculating the infrared spectra of liquid water at standard conditions, and of ice under extreme pressure, when it transforms from a molecular to an ionic crystal.