Dynamic Collision Fingerprints (DCF): Introducing a New Descriptor Linking Lattice Interactions to 2D Structural Data...

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Nanoscience

Background:

• Characterizing two-dimensional (2D) materials is crucial for advanced applications.
• Existing methods may be computationally intensive or limited in scope.
• Novel, efficient, and generalizable techniques are needed for structural analysis.

Purpose of the Study:

• Introduce a new computational method for the structural characterization of 2D materials.
• Develop a technique that utilizes classical particle dynamics and elastic collisions.
• Encode structural information through statistical collision patterns.

Main Methods:

• Developed the dynamic collision fingerprint (DCF) method.
• Simulated classical particle trajectories and elastic collisions with atomic lattices.
• Extracted geometric features including mean free path, diffusivity, and symmetry metrics.

Main Results:

• DCF successfully captures key structural features: symmetry, porosity, and disorder.
• Applied to graphene, phagraphene, CEY-graphene, and hexagonal boron nitride (h-BN).
• Generated compact, interpretable descriptor vectors suitable for machine learning and classification.

Conclusions:

• DCF offers a robust and generalizable approach for 2D materials analysis.
• The method is computationally efficient, runnable on a personal computer.
• DCF is particularly effective for carbon-based and atomically thin materials.