The Phase Diagram of a Deep Potential Water Model

Abstract

Using the Deep Potential methodology, we construct a model that reproduces accurately the potential energy surface of the SCAN approximation of density functional theory for water, from low temperature and pressure to about 2400 K and 50 GPa, excluding the vapor stability region. The computational efficiency of the model makes it possible to predict its phase diagram using molecular dynamics. Satisfactory overall agreement with experimental results is obtained. The fluid phases, molecular and ionic, and all the stable ice polymorphs, ordered and disordered, are predicted correctly, with the exception of ice III and XV that are stable in experiments, but metastable in the model. The evolution of the atomic dynamics upon heating, as ice VII transforms first into ice VII${}^{\prime \prime }$ and then into an ionic fluid, reveals that molecular dissociation and breaking of the ice rules coexist with strong covalent fluctuations, explaining why only partial ionization was inferred in experiments.

References in corpus (19)

• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
• Machine Learning Molecular Dynamics for the Simulation of Infrared Spectra
• Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation
• How van der Waals interactions determine the unique properties of water
• Ab initio theory and modeling of water
• Ab initio thermodynamics of liquid and solid water
• Why does hydronium diffuse faster than hydroxide in liquid water?
• Determination of phase diagrams via computer simulation: Methodology and applications to water, electrolytes and proteins
• Ice XV: a new thermodynamically stable phase of ice
• Deep neural network for the dielectric response of insulators
• Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks
• Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals
• The phase diagram of water at high pressures as obtained by computer simulations of the TIP4P/2005 model: the appearance of a plastic crystal phase
• Isotope Effects in Liquid Water via Deep Potential Molecular Dynamics
• Quantum-mechanical exploration of the phase diagram of water
• Ab-initio Structure and Thermodynamics of the RPBE-D3 Water/Vapor Interface by Neural-Network Molecular Dynamics
• Proton ordering dynamics of H2O ice

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