In spite of the great importance of this system and the large number of studies based on density-functional theory (DFT) and ab initio molecular dynamics (AIMD) devoted to it 14, 15, 16, 17, 18, 19, 20, 21, 22, all of these efforts have, until very recently 23, dodged its viscous properties, because an accurate computation of the viscosity of water would require exceedingly long first-principles simulations 20. In this work, we focus on water, an ubiquitous molecular liquid with extraordinary and complex properties 8, 9, 10, 11, 12, 13. An accurate determination of the temperature and pressure profile of the viscosity is also essential for the correct modeling of tidal interactions in the planets’ interior, in particular in the presence of icy layers 6, 7. The value of the viscosity of liquid iron, abundant in Earth’s outer core, is key in the prediction of the magnetic field of rocky planets 4, 5. For instance, the viscosity of a solvent crucially affects the dynamics of solutes and the reactions rates, of fundamental importance in the study of biological processes and chemical reactions 1, 2, 3. As such, it plays a fundamental role in various fields of science and technology, such as, e.g., chemical and mechanical engineering or earth and planetary sciences, to name but a few. Shear viscosity is one of the most important transport properties governing the macroscopic flow of liquids. 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.
Then, we train a second NNP to a dataset generated from the Strongly Constrained and Appropriately Normed (SCAN) functional. 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. 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).
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).
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