Machine learning dielectric constant of water in a large pressure-temperature range

The dielectric properties of supercritical water in Earth's interior play an important role in determining how it stores and transports materials. However, obtaining the static dielectric constant of water, ε₀, is very challenging in a wide pressure-temperature (P-T) range as found in deep Earth either experimentally or by first-principles simulations. Here, we built a neural network dipole model, which can be combined with molecular dynamics to compute P-T dependent dielectric properties of water as accurately as first-principles methods but much more efficiently. We found that ε₀ may vary by one order of magnitude in Earth's upper mantle, indicating the solvation properties of water change dramatically at different depths. The competing effects between molecular dipole moment and the dipolar angular correlation govern the change of ε₀. We also calculated the frequency-dependent dielectric constant of water in the microwave range, suggesting 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. (J. Chem. Phys. 153, 101103 (2020))