The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Phase Diagram of a Deep Potential Water Model
Open accessAbstract
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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Cites
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A general purpose model for the condensed phases of water: TIP4P/2005
Abascal, J. L. F., & Vega, C. (2005) · The Journal of Chemical Physics · doi:10.1063/1.2121687 · Cited by 3,964
Strongly Constrained and Appropriately Normed Semilocal Density Functional
Sun, J., Ruzsinszky, A., & Perdew, J. P. (2015) · Physical Review Letters · doi:10.1103/physrevlett.115.036402 · Cited by 3,596
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Zhang, L., Han, J., Wang, H., Car, R., & E, W. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.143001 · Cited by 2,258
Self-citationWang, H., Zhang, L., Han, J., & E, W. (2018) · Computer Physics Communications · doi:10.1016/j.cpc.2018.03.016 · Cited by 2,138
Self-citationThe nature of the hydrated excess proton in water
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CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
Deng, B., Zhong, P., Jun, K., Riebesell, J., Han, K., Bartel, C. J., & Ceder, G. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00716-3 · Cited by 944
Predicting Structural Properties of Pure Silica Zeolites Using Deep Neural Network Potentials
Sours, T. G., & Kulkarni, A. R. (2023) · The Journal of Physical Chemistry C · doi:10.1021/acs.jpcc.2c08429 · Cited by 36
Proton Collective Quantum Tunneling Induces Anomalous Thermal Conductivity of Ice under Pressure
Wang, Y., Luo, R., Chen, J., Zhou, X., Wang, S., Wu, J., Kang, F., Yu, K., & Sun, B. (2024) · Physical Review Letters · doi:10.1103/physrevlett.132.264101 · Cited by 10
Metadata
- Authors
- Zhang, L., Wang, H., Car, R., & E, W.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2021-06-09
- Volume
- 126
- Issue
- 23
- Pages
- 236001-236001
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W3126533621
- Cited by
- 334
- References
- 44
- FWCI
- 32.84
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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