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A new topological descriptor for water network structure.

Lee Steinberg1, John Russo2, Jeremy Frey3

  • 1School of Chemistry, University of Southampton, Southampton, SO17 1BJ, UK.

Journal of Cheminformatics
|July 12, 2019
PubMed
Summary
This summary is machine-generated.

Topological data analysis using L1 normalized persistence images (L1NPI) effectively distinguishes between different water models in molecular dynamics simulations. This size-independent descriptor reveals structural differences and tracks interaction parameters, offering a new tool for materials analysis.

Keywords:
Persistent homologyTopological data analysisWater networks

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Area of Science:

  • Computational chemistry
  • Materials science
  • Topological data analysis

Background:

  • Molecular dynamics simulations are crucial for understanding water behavior.
  • Existing methods for analyzing simulation data can be limited, especially for systems with varying molecule counts.
  • Atomistic water potentials (e.g., TIP3P, TIP4P/Ew, SPC/E, OPC) yield different structural and dynamic properties.

Purpose of the Study:

  • To introduce and validate a novel, size-independent descriptor, the L1 normalized persistence image (L1NPI), for analyzing molecular dynamics simulations of water.
  • To demonstrate the efficacy of L1NPI in differentiating between various atomistic water models.
  • To explore the application of L1NPI in analyzing the impact of interaction potentials, such as the Stillinger-Weber potential, on water structure.

Main Methods:

  • Application of topological data analysis techniques to atomic positions from molecular dynamics simulations.
  • Calculation of topological invariants (homology degrees) and their representation in persistence diagrams.
  • Averaging persistence diagrams over simulation time using the persistence image formalism and normalizing by the L1 norm to create the L1NPI descriptor.
  • Utilizing machine learning (linear Support Vector Machine) to analyze the L1NPIs and identify differences between water models.

Main Results:

  • The L1NPI formalism successfully distinguishes between different atomistic water potentials, with varying degrees of homology capturing specific differences.
  • First-degree homology differentiates all tested potentials, while second-degree homology uniquely identifies the OPC potential.
  • L1NPI analysis clearly shows the effect of varying three-body interaction strengths in the Stillinger-Weber potential, with increased strength reducing structural variance.
  • The L1NPI descriptor effectively tracks the lambda parameter in simulations.

Conclusions:

  • The L1NPI formalism is a robust and versatile new technique for analyzing molecular dynamics simulations of water and other materials.
  • The descriptor is approximately size-independent and captures significant structural information.
  • L1NPIs show promise as descriptors for properties like water solubility, warranting further investigation.