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Cluster Identification Using Modularity Optimization to Uncover Chemical Heterogeneity in Complex Solutions.

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This study introduces a new network analysis method to identify distinct chemical environments in liquids. The cluster partitioning approach effectively reveals structural heterogeneity across multiple scales in soft matter systems.

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Area of Science:

  • Soft Matter Physics
  • Computational Chemistry
  • Network Science

Background:

  • Structural heterogeneity in liquids arises from competing interparticle forces, requiring advanced methods for characterization across length scales.
  • Interparticle interaction networks offer insights into chemical environments, but diverse network topologies challenge generalizable analysis.
  • Existing network analysis methods often lack multiscale resolution for complex systems.

Purpose of the Study:

  • To develop and validate a generalizable network partitioning method for identifying structural heterogeneity in soft matter systems.
  • To apply graph theory concepts, specifically cluster and community detection, for multiscale analysis of interparticle interaction networks.
  • To demonstrate the method's efficacy on systems with distinct network topologies, including a binary fluid and water at an interface.

Main Methods:

  • Utilized a modularity optimization algorithm based on graph theory to partition interparticle interaction networks into clusters.
  • Applied the cluster partitioning methodology to a binary Lennard-Jones fluid to analyze subgraph connectivity.
  • Tested the method on hydrogen-bonded water molecules at a liquid/liquid interface, including a time-dependent analysis.

Main Results:

  • The cluster partition successfully identified subensembles with high internal connectivity in the binary fluid, revealing the impact of network properties.
  • Hierarchically organized water structures at the interface were isolated from the background network, despite sparse and varied internal connectivity.
  • Time-dependent analysis of the water system revealed the reactivity of these isolated macrostructures.

Conclusions:

  • Cluster partitioning based on intermolecular network connectivity is a broadly generalizable technique for identifying heterogeneity.
  • The method operates across different length scales and is adaptable to dynamic phenomena.
  • This approach provides a powerful tool for interrogating atomistic simulation data in soft matter research.