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This study introduces persistences for coarse-graining potential energy landscapes. High persistence minima reveal alternative structural morphologies, simplifying complex network analysis.

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Area of Science:

  • Computational Chemistry
  • Biophysics
  • Materials Science

Background:

  • High-dimensional potential energy landscapes are crucial for understanding molecular systems.
  • Analyzing these landscapes often involves complex kinetic transition networks.

Purpose of the Study:

  • To develop a coarse-graining method for potential energy landscapes using persistences.
  • To identify and analyze alternative structural morphologies within these landscapes.
  • To simplify the representation of complex kinetic networks.

Main Methods:

  • Calculation of persistences (lowest barrier heights) for local minima in kinetic transition networks.
  • Using persistence thresholds to select relevant minima.
  • Analysis of selected minima to represent features of the full network.
  • Construction of simplified disconnectivity graphs.

Main Results:

  • Persistences efficiently characterize local minima in kinetic networks.
  • High persistence minima indicate significant alternative structural morphologies.
  • Simplified disconnectivity graphs effectively represent funnelling characteristics.
  • Persistence thresholds aid in comparing networks under different conditions.

Conclusions:

  • Persistence-based coarse-graining offers an efficient method for analyzing complex energy landscapes.
  • This approach simplifies the visualization and comparison of molecular structures and dynamics.
  • The method is applicable to diverse systems including biomolecules and materials.