How van der Waals interactions determine the unique properties of water

By Tobias Morawietz, A. Singraber, Christoph Dellago, J. Behler. 2016

416 citations recorded by the source corpus.

Significance Despite its simple chemical formula, H2O, water is a complex substance with a variety of unusual properties resulting from its ability to form hydrogen bonds. A famous example for the anomalous behavior of water is the fact that it exhibits a density maximum at 4 °C. Here, we unravel the density anomaly of water on the molecular level using a powerful ab initio-based simulation technique. We show that weak van der Waals forces crucially modulate the flexibility of the hydrogen bond network, giving rise to the density maximum in water and causing ice to be less dense than the liquid. Whereas 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.

Subfields: Atomic and Molecular Physics, and Optics, Statistical and Nonlinear Physics

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Citations in the corpus

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns (2019, methodology): “These hydrogen bonds weaken when ice melts, and water molecules approximate, becoming dense with an extreme value at 4 ◦ C (Morawietz et al., 2016).”
DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics (2017, neutral): “{ "n_neuron": [240, 120, 60, 30, 10], "systems": ["/path/to/water", "/path/to/ice"], "stop_batch": 1000000, "batch_size": 4,”
Emerging materials intelligence ecosystems propelled by machine learning (2020, background): “133 ) ), alloys 134 , metallic clusters 135,136 , semiconductors 137 , oxides 138,139 , water 140,141 and organic molecules 142,143 , and complex phenomena, such as diffusion 144 and phase equilibrium.”