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Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car, and Weinan E
Phys. Rev. Lett. 126, 236001 – Published 9 June 2021
Physics logo See synopsis: An Efficient Way to Predict Water’s Phases
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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′′ 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.

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  • 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)

Statistical Physics & ThermodynamicsCondensed Matter, Materials & Applied Physics

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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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Authors & Affiliations

Linfeng Zhang

  • Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA

Han Wang*

  • Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China

Roberto Car

  • Department of Chemistry, Department of Physics, Program in Applied and Computational Mathematics, Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA

Weinan E

  • Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA and Beijing Institute of Big Data Research, Beijing 100871, People’s Republic of China

  • *wang_han@iapcm.ac.cn
  • rcar@princeton.edu

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Issue

Vol. 126, Iss. 23 — 11 June 2021

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