Viscosity in water from first-principles and deep-neural-network simulations

TL;DR

This study combines ab initio molecular dynamics with deep neural network potentials to accurately predict the viscosity of water at near-ambient conditions, validated against experimental data and different DFT functionals.

Contribution

It introduces a validated deep neural network approach to efficiently simulate water viscosity using first-principles data, improving accuracy over traditional AIMD methods.

Findings

Deep neural network potentials enable longer simulations with high accuracy.

SCAN functional-based predictions align well with experimental viscosity data.

Temperature correction based on melting point improves prediction accuracy.

Abstract

We report on an extensive study of the viscosity of liquid water at near-ambient conditions, performed within the Green-Kubo theory of linear response and equilibrium ab initio molecular dynamics (AIMD), based on density-functional theory (DFT). In order to cope with the long simulation times necessary to achieve an acceptable statistical accuracy, our ab initio approach is enhanced with deep-neural-network potentials (NNP). This approach is first validated against AIMD results, obtained by using the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional and paying careful attention to crucial, yet often overlooked, aspects of the statistical data analysis. Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. Once the error resulting from the imperfect prediction of the melting line is offset by referring the simulated temperature to the theoretical melting one, our SCAN predictions of the shear viscosity of water are in very good agreement with experiments.