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 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.
- Received 11 February 2021
- Accepted 28 April 2021
DOI:https://doi.org/10.1103/PhysRevLett.126.236001
© 2021 American Physical Society
Physics Subject Headings (PhySH)
synopsis
An Efficient Way to Predict Water’s Phases
Published 9 June 2021
A machine-learning technique maps water’s phase space as reliably as gold standard ab initio calculations but at a much smaller computational cost.
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