Dielectric constant of supercritical water in a large pressure-temperature range

TL;DR

This study introduces a neural network-based method combined with molecular dynamics to accurately compute the dielectric constant of supercritical water across a wide P-T range, revealing significant variations relevant to Earth's interior and other fields.

Contribution

The paper presents a novel neural network dipole model that efficiently predicts dielectric properties of water under extreme conditions, outperforming traditional first-principles simulations.

Findings

Dielectric constant varies by an order of magnitude in Earth's upper mantle.

Temperature influences dielectric absorption more than pressure.

Frequency-dependent dielectric properties were calculated for the first time from first principles.

Abstract

A huge amount of water at supercritical conditions exists in Earth's interior, where its dielectric properties play a critical role in determining how it stores and transports materials. However, it is very challenging to obtain the static dielectric constant of water, $\epsilon_0$, in a wide pressure-temperature (P-T) range as found in deep Earth either experimentally or by first-principles simulations. Here, we introduce a neural network dipole model, which, combined with molecular dynamics, can be used to compute P-T dependent dielectric properties of water as accurately as first-principles methods but much more efficiently. We found that $\epsilon_0$ may vary by one order of magnitude in Earth's upper mantle, suggesting that the solvation properties of water change dramatically at different depths. There is a subtle interplay between the molecular dipole moment and the dipolar angular correlation in governing the change of $\epsilon_0$. We also calculated the frequency-dependent dielectric constant of water in the microwave range, which, to the best of our knowledge, has not been calculated from first principles, and found that temperature affects the dielectric absorption more than pressure. Our results are of great use in many areas, e.g., modelling water-rock interactions in geochemistry. The computational approach introduced here can be readily applied to other molecular fluids.