A neural network potential-energy surface for the water dimer based on environment-dependent atomic energies and charges

Morawietz, Tobias; Sharma, Vikas; Behler, Jörg

doi:doi:10.1063/1.3682557

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Understanding the unique properties of water still represents a significant challenge for theory and experiment. Computer simulations by molecular dynamics require a reliable description of the atomic interactions, and in recent decades countless water potentials have been reported in the literature. Still, most of these potentials contain significant approximations, for instance a frozen internal structure of the individual water monomers. Artificial neural networks (NNs) offer a promising way for the construction of very accurate potential-energy surfaces taking all degrees of freedom explicitly into account. These potentials are based on electronic structure calculations for representative configurations, which are then interpolated to a continuous energy surface that can be evaluated many orders of magnitude faster. We present a full-dimensional NN potential for the water dimer as a first step towards the construction of a NN potential for liquid water. This many-body potential is based on environment-dependent atomic energy contributions, and long-range electrostatic interactions are incorporated employing environment-dependent atomic charges. We show that the potential and derived properties like vibrational frequencies are in excellent agreement with the underlying reference density-functional theory calculations.

Article Outline

INTRODUCTION
NEURAL NETWORK POTENTIALS
1. High-dimensional neural network potentials
2. Long-range electrostatic interactions
RESULTS AND DISCUSSION
1. Reference calculations
2. Systematic construction of the training set
3. Accuracy of the neural network potential
4. Stationary points on the dimer potential-energy surface
5. Molecular dynamics
CONCLUSIONS

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KEYWORDS, PACS, and IPC

Keywords

chemistry computing, density functional theory, molecular dynamics method, molecular electronic states, neural nets, potential energy surfaces, vibrational states, water

PACS

31.50.-x

Potential energy surfaces
33.20.Tp

Vibrational analysis
34.20.-b

Interatomic and intermolecular potentials and forces, potential energy surfaces for collisions
31.15.E-

Density-functional theory
31.15.xv

Molecular dynamics and other numerical methods

International Patent Classification (IPC)

G06F19/00
Digital computing or data processing equipment or methods, specially adapted for specific applications

ARTICLE DATA

Digital Object Identifier

http://dx.doi.org/10.1063/1.3682557

PUBLICATION DATA

ISSN

0021-9606 (print)

1089-7690 (online)

Publisher

$abstract.society.value is a Member of CrossRef

American Institute of Physics

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