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Title: First-principles energetics of water clusters and ice: A many-body analysis

Journal Article · · Journal of Chemical Physics
DOI:https://doi.org/10.1063/1.4852182· OSTI ID:22253235
 [1]; ;  [2]
  1. London Centre for Nanotechnology, UCL, London WC1H 0AH (United Kingdom)
  2. Department of Engineering, University of Cambridge, Cambridge (United Kingdom)

Standard forms of density-functional theory (DFT) have good predictive power for many materials, but are not yet fully satisfactory for cluster, solid, and liquid forms of water. Recent work has stressed the importance of DFT errors in describing dispersion, but we note that errors in other parts of the energy may also contribute. We obtain information about the nature of DFT errors by using a many-body separation of the total energy into its 1-body, 2-body, and beyond-2-body components to analyze the deficiencies of the popular PBE and BLYP approximations for the energetics of water clusters and ice structures. The errors of these approximations are computed by using accurate benchmark energies from the coupled-cluster technique of molecular quantum chemistry and from quantum Monte Carlo calculations. The systems studied are isomers of the water hexamer cluster, the crystal structures Ih, II, XV, and VIII of ice, and two clusters extracted from ice VIII. For the binding energies of these systems, we use the machine-learning technique of Gaussian Approximation Potentials to correct successively for 1-body and 2-body errors of the DFT approximations. We find that even after correction for these errors, substantial beyond-2-body errors remain. The characteristics of the 2-body and beyond-2-body errors of PBE are completely different from those of BLYP, but the errors of both approximations disfavor the close approach of non-hydrogen-bonded monomers. We note the possible relevance of our findings to the understanding of liquid water.

OSTI ID:
22253235
Journal Information:
Journal of Chemical Physics, Vol. 139, Issue 24; Other Information: (c) 2013 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA); ISSN 0021-9606
Country of Publication:
United States
Language:
English

Cited By (6)

Gaussian approximation potentials: A brief tutorial introduction journal April 2015
Data sampling scheme for reproducing energies along reaction coordinates in high-dimensional neural network potentials journal April 2019
Accuracy and transferability of Gaussian approximation potential models for tungsten journal September 2014
Machine Learning a General-Purpose Interatomic Potential for Silicon journal December 2018
Machine Learning a General-Purpose Interatomic Potential for Silicon text January 2018
Gaussian Approximation Potentials: a brief tutorial introduction preprint January 2015

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