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Graph-based descriptors for condensed matter.

An Wang1, Gabriele C Sosso1

  • 1University of Warwick, Department of Chemistry, Coventry CV4 7AL, United Kingdom.

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|August 1, 2025
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Summary
This summary is machine-generated.

This study introduces graph-based descriptors from network science for condensed matter systems. These new methods outperform traditional descriptors in predicting dynamical properties and phase transitions.

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Area of Science:

  • Condensed matter physics
  • Computational materials science
  • Network science

Background:

  • Traditional descriptors for condensed matter systems often rely on spatial configurations.
  • Symmetry functions and smooth overlap of atomic positions (SOAP) are established descriptors.
  • Graph-based descriptors are underutilized in condensed matter despite success in small molecule prediction.

Purpose of the Study:

  • To explore the application of graph-based descriptors from network science to condensed matter systems.
  • To evaluate the performance of these novel descriptors against existing methods.
  • To investigate dynamical properties and phase transitions in condensed matter.

Main Methods:

  • Utilized graph-based descriptors such as node centrality and local clustering coefficients.
  • Applied these descriptors to represent condensed matter systems.
  • Investigated the prototypical Lennard-Jones system to test the formalism.

Main Results:

  • Graph-based descriptors demonstrated effectiveness in representing condensed matter.
  • The proposed graph-based formalism outperformed symmetry function descriptors.
  • Accurate prediction of dynamical properties and phase transitions was achieved.

Conclusions:

  • Graph-based features offer a powerful alternative for representing condensed matter systems.
  • Integrating network science concepts can drive advancements in condensed matter research.
  • This approach broadens the applicability of machine learning in materials science.