How van der Waals interactions determine the unique properties of water
arXiv:1606.07775 · doi:10.1073/pnas.1602375113
Abstract
While the interactions between water molecules are dominated by strongly directional hydrogen bonds (HBs), it was recently proposed that relatively weak, isotropic van der Waals (vdW) forces are essential for understanding the properties of liquid water and ice. This insight was derived from ab initio computer simulations, which provide an unbiased description of water at the atomic level and yield information on the underlying molecular forces. However, the high computational cost of such simulations prevents the systematic investigation of the influence of vdW forces on the thermodynamic anomalies of water. Here we develop efficient ab initio-quality neural network potentials and use them to demonstrate that vdW interactions are crucial for the formation of water's density maximum and its negative volume of melting. Both phenomena can be explained by the flexibility of the HB network, which is the result of a delicate balance of weak vdW forces, causing e.g. a pronounced expansion of the second solvation shell upon cooling that induces the density maximum.
20 pages, 15 figures, accepted by Proc. Natl. Acad. Sci. USA
References in corpus (4)
- Competing quantum effects in the dynamics of a flexible water model
- Nuclear quantum effects in water
- The Individual and Collective Effects of Exact Exchange and Dispersion Interactions on the Ab Initio Structure of Liquid Water
- How the Liquid-Liquid Transition Affects Hydrophobic Hydration in Deeply Supercooled Water
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