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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...

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Autores principales: Steinberg, Lee, Russo, John, Frey, Jeremy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617667/
https://www.ncbi.nlm.nih.gov/pubmed/31292766
http://dx.doi.org/10.1186/s13321-019-0369-0
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author Steinberg, Lee
Russo, John
Frey, Jeremy
author_facet Steinberg, Lee
Russo, John
Frey, Jeremy
author_sort Steinberg, Lee
collection PubMed
description 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-019-0369-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-66176672019-07-22 A new topological descriptor for water network structure Steinberg, Lee Russo, John Frey, Jeremy J Cheminform Research Article 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13321-019-0369-0) contains supplementary material, which is available to authorized users. Springer International Publishing 2019-07-10 /pmc/articles/PMC6617667/ /pubmed/31292766 http://dx.doi.org/10.1186/s13321-019-0369-0 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Steinberg, Lee
Russo, John
Frey, Jeremy
A new topological descriptor for water network structure
title A new topological descriptor for water network structure
title_full A new topological descriptor for water network structure
title_fullStr A new topological descriptor for water network structure
title_full_unstemmed A new topological descriptor for water network structure
title_short A new topological descriptor for water network structure
title_sort new topological descriptor for water network structure
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617667/
https://www.ncbi.nlm.nih.gov/pubmed/31292766
http://dx.doi.org/10.1186/s13321-019-0369-0
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