Abstract
Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity. It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. Here we investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The finding that the local environments characterising the different ice phases are found in water sheds light on the phase behavior of water, and rationalizes the transferability of water models between different phases.
Highlights
Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity
The various ice phases in the complex phase diagram of water are made from distinct local atomic environments[1], which lead to a large spread in their densities, lattice energies, and other thermodynamic as well as kinetic properties[1,6]
More structure–property relationships may be extracted from this principal component analysis (PCA) map, and we provide an interactive explorer of the ice and liquid water dataset in the Supplementary Data 1 (“Supplementary Data 1”), which runs in web browser and is made using Chemiscope[16]
Summary
Water molecules can arrange into a liquid with complex hydrogen-bond networks and at least 17 experimentally confirmed ice phases with enormous structural diversity It remains a puzzle how or whether this multitude of arrangements in different phases of water are related. We investigate the structural similarities between liquid water and a comprehensive set of 54 ice phases in simulations, by directly comparing their local environments using general atomic descriptors, and by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, lattice energies, and vibrational properties of the ices. The various ice phases in the complex phase diagram of water are made from distinct local atomic environments[1], which lead to a large spread in their densities, lattice energies, and other thermodynamic as well as kinetic properties[1,6]. The MLP trained using these structures reproduces many properties of water very well, including the density isobar and radial distribution functions at ambient pressure[11], which means that the training set contains the necessary information for describing liquid water at ambient pressure in a data-driven manner
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