Issue 13, 2015

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials

Abstract

Investigating the properties of protons in water is essential for understanding many chemical processes in aqueous solution. While important insights can in principle be gained by accurate and well-established methods like ab initio molecular dynamics simulations, the computational costs of these techniques are often very high. This prevents studying large systems on long time scales, which is severely limiting the applicability of computer simulations to address a wide range of interesting phenomena. Developing more efficient potentials enabling the simulation of water including dissociation and recombination events with first-principles accuracy is a very challenging task. In particular protonated water clusters have become important model systems to assess the reliability of such potentials, as the presence of the excess proton induces substantial changes in the local hydrogen bond patterns and many energetically similar isomers exist, which are extremely difficult to describe. In recent years it has been demonstrated for a number of systems including neutral water clusters of varying size that neural networks (NNs) can be used to construct potentials with close to first-principles accuracy. Based on density-functional theory (DFT) calculations, here we present a reactive full-dimensional NN potential for protonated water clusters up to the octamer. A detailed investigation of this potential shows that the energetic, structural, and vibrational properties are in excellent agreement with DFT results making the NN approach a very promising candidate for developing a high-quality potential for water. This finding is further supported by first preliminary but very encouraging NN-based simulations of the bulk liquid.

Graphical abstract: Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials

Supplementary files

Article information

Article type
Paper
Submitted
18 ១០ 2014
Accepted
20 ១១ 2014
First published
21 ១១ 2014

Phys. Chem. Chem. Phys., 2015,17, 8356-8371

Author version available

Representing the potential-energy surface of protonated water clusters by high-dimensional neural network potentials

S. Kondati Natarajan, T. Morawietz and J. Behler, Phys. Chem. Chem. Phys., 2015, 17, 8356 DOI: 10.1039/C4CP04751F

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements