Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Zeitschrift für Physikalische Chemie

International journal of research in physical chemistry and chemical physics

Editor-in-Chief: Rademann, Klaus


IMPACT FACTOR 2018: 0.975
5-year IMPACT FACTOR: 1.021

CiteScore 2018: 1.20

SCImago Journal Rank (SJR) 2018: 0.327
Source Normalized Impact per Paper (SNIP) 2018: 0.391

Online
ISSN
2196-7156
See all formats and pricing
More options …
Volume 227, Issue 9-11

Issues

A Full-Dimensional Neural Network Potential-Energy Surface for Water Clusters up to the Hexamer

Tobias Morawietz / Jörg Behler
Published Online: 2013-08-05 | DOI: https://doi.org/10.1524/zpch.2013.0384

Abstract

Water clusters have attracted a lot of attention as prototype systems to study hydrogen bonded molecular aggregates but also to gain deeper insights into the properties of liquid water, the solvent of life. All these studies depend on an accurate description of the atomic interactions and countless potentials have been proposed in the literature in the past decades to represent the potential-energy surface (PES) of water. Many of these potentials employ drastic approximations like rigid monomers and fixed point charges, while on the other hand also several attempts have been made to derive very accurate PESs by fitting data obtained in high-level electronic structure calculations. In recent years artificial neural networks (NNs) have been established as a powerful tool to construct high-dimensional PESs of a variety of systems, but to date no full-dimensional NN PES for has been reported. Here, we present NN potentials for clusters containing two to six molecules trained to density functional theory (DFT) data employing two different exchange-correlation functionals, PBE and RPBE. In contrast to other potentials fitted to first principles data, these NN potentials are not based on a truncated many-body expansion of the energy but consider the interactions between all molecules explicitly. For both functionals an excellent agreement with the underlying DFT calculations has been found with binding energy errors of only about 1%.

Keywords: Potential Energy Surface; Neural Network Potentials; Water; Density-Functional Theory; Molecular Dynamics

About the article

Received: 2012-12-21

Published Online: 2013-08-05

Published in Print: 2013-11-01


Citation Information: Zeitschrift für Physikalische Chemie, Volume 227, Issue 9-11, Pages 1559–1581, ISSN (Online) 2196-7156, ISSN (Print) 0942-9352, DOI: https://doi.org/10.1524/zpch.2013.0384.

Export Citation

© 2013 by Walter de Gruyter Berlin Boston. This article is distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. BY-NC-ND 4.0

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Andreas Singraber, Tobias Morawietz, Jörg Behler, and Christoph Dellago
Journal of Chemical Theory and Computation, 2019
[2]
Andreas Singraber, Jörg Behler, and Christoph Dellago
Journal of Chemical Theory and Computation, 2019
[3]
Ivan Sukuba, Lei Chen, Michael Probst, and Alexander Kaiser
Molecular Simulation, 2018, Page 1
[6]
Tien Lam Pham, Hiori Kino, Kiyoyuki Terakura, Takashi Miyake, and Hieu Chi Dam
The Journal of Chemical Physics, 2016, Volume 145, Number 15, Page 154103
[7]
Brian Kolb, Bin Zhao, Jun Li, Bin Jiang, and Hua Guo
The Journal of Chemical Physics, 2016, Volume 144, Number 22, Page 224103
[8]
Tobias Morawietz and Jörg Behler
The Journal of Physical Chemistry A, 2013, Volume 117, Number 32, Page 7356
[10]
Michael Gastegger and Philipp Marquetand
Journal of Chemical Theory and Computation, 2015, Volume 11, Number 5, Page 2187
[11]
Suresh Kondati Natarajan, Tobias Morawietz, and Jörg Behler
Phys. Chem. Chem. Phys., 2015, Volume 17, Number 13, Page 8356
[12]
Jörg Behler
International Journal of Quantum Chemistry, 2015, Volume 115, Number 16, Page 1032
[13]
Sergei Manzhos, Richard Dawes, and Tucker Carrington
International Journal of Quantum Chemistry, 2015, Volume 115, Number 16, Page 1012
[14]
Christopher Michael Handley and Jörg Behler
The European Physical Journal B, 2014, Volume 87, Number 7
[15]
J Behler
Journal of Physics: Condensed Matter, 2014, Volume 26, Number 18, Page 183001

Comments (0)

Please log in or register to comment.
Log in