The University of Southampton
University of Southampton Institutional Repository

A new topological descriptor for water network structure

A new topological descriptor for water network structure
A new topological descriptor for water network structure
Bulk water molecular dynamics simulations based on a series of atomistic water potentials (TIP3P, TIP4P/Ew, SPC/E and OPC) are compared using new techniques from the field of topological data analysis. The topological invariants (the different degrees of homology) derived from each simulation frame are used to create a series of persistence diagrams from the atomic positions. These are averaged over the simulation time using the persistence image formalism, before being normalised by their total magnitude (the L1 norm) to ensure a size independent descriptor (L1NPI). We demonstrate that the L1NPI formalism is suitable for the analysis of systems where the number of molecules varies by at least a factor of 10. Using standard machine learning techniques, a basic linear SVM, it is shown that differences in water models are able to be isolated to different degrees of homology. In particular, whereas first degree homology is able to distinguish between all atomistic potentials studied, OPC is the only potential that differs in its second degree homology. The L1 normalised persistence images are then used in the comparison of a series of Stillinger–Weber potential simulations to the atomistic potentials and the effects of changing the strength of three-body interactions on the structures is easily evident in L1NPI space, with a reduction in variance of structures as interaction strength increases being the most obvious result. Furthermore, there is a clear tracking in L1NPI space of the λ parameter. The L1NPI formalism presents a useful new technique for the analysis of water and other materials. It is approximately size-independent, and has been shown to contain information as to real structures in the system. We finally present a perspective on the use of L1NPIs and other persistent homology techniques as a descriptor for water solubility.
Persistent homology, Water networks, topological data analysis
1758-2946
1-11
Steinberg, Lee
283f7d74-c02e-4f52-a59e-396b12239e02
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Russo, John
83ed1a4f-369c-43c5-9477-708d4756ba5f
Steinberg, Lee
283f7d74-c02e-4f52-a59e-396b12239e02
Frey, Jeremy G.
ba60c559-c4af-44f1-87e6-ce69819bf23f
Russo, John
83ed1a4f-369c-43c5-9477-708d4756ba5f

Steinberg, Lee, Frey, Jeremy G. and Russo, John (2019) A new topological descriptor for water network structure. Journal of Cheminformatics, 11 (48), 1-11. (doi:10.1186/s13321-019-0369-0).

Record type: Article

Abstract

Bulk water molecular dynamics simulations based on a series of atomistic water potentials (TIP3P, TIP4P/Ew, SPC/E and OPC) are compared using new techniques from the field of topological data analysis. The topological invariants (the different degrees of homology) derived from each simulation frame are used to create a series of persistence diagrams from the atomic positions. These are averaged over the simulation time using the persistence image formalism, before being normalised by their total magnitude (the L1 norm) to ensure a size independent descriptor (L1NPI). We demonstrate that the L1NPI formalism is suitable for the analysis of systems where the number of molecules varies by at least a factor of 10. Using standard machine learning techniques, a basic linear SVM, it is shown that differences in water models are able to be isolated to different degrees of homology. In particular, whereas first degree homology is able to distinguish between all atomistic potentials studied, OPC is the only potential that differs in its second degree homology. The L1 normalised persistence images are then used in the comparison of a series of Stillinger–Weber potential simulations to the atomistic potentials and the effects of changing the strength of three-body interactions on the structures is easily evident in L1NPI space, with a reduction in variance of structures as interaction strength increases being the most obvious result. Furthermore, there is a clear tracking in L1NPI space of the λ parameter. The L1NPI formalism presents a useful new technique for the analysis of water and other materials. It is approximately size-independent, and has been shown to contain information as to real structures in the system. We finally present a perspective on the use of L1NPIs and other persistent homology techniques as a descriptor for water solubility.

Text
s13321-019-0369-0 - Version of Record
Available under License Creative Commons Attribution.
Download (2MB)

More information

Submitted date: 2018
Accepted/In Press date: 2 July 2019
Published date: 10 July 2019
Keywords: Persistent homology, Water networks, topological data analysis

Identifiers

Local EPrints ID: 432376
URI: https://eprints.soton.ac.uk/id/eprint/432376
ISSN: 1758-2946
PURE UUID: 8ca0370e-3027-48fa-8880-15d30ef0cd3e
ORCID for Jeremy G. Frey: ORCID iD orcid.org/0000-0003-0842-4302

Catalogue record

Date deposited: 11 Jul 2019 16:30
Last modified: 29 Aug 2019 00:56

Export record

Altmetrics

Contributors

Author: Lee Steinberg
Author: Jeremy G. Frey ORCID iD
Author: John Russo

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×