Work

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

Year: 2025

Type: article

Abstract: We introduce a novel method for structural characterization of two-dimensional (2D) materials based on classical particle trajectories and elastic collisions with atomic lattices. This approach, called the dynamic collision fingerprint (DCF), encodes structural features through statistical patterns of collisions, capturing symmetry, porosity, and disorder using purely geometric information. Applied to systems such as graphene, phagraphene, CEY-graphene, and h-BN, the method extracts descriptors, including mean free path, diffusivity, angular entropy, and Fourier-based symmetry metrics. These form compact interpretable vectors suitable for classification and machine learning. All calculations were performed on a personal computer, underscoring the method's efficiency and accessibility. DCF provides a robust and general tool for 2D materials analysis, particularly well-suited for carbon-based and atomically thin systems.

Source: Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c00856

Author: Raphael M. Tromer

Institution: Universidade de Brasília

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funders: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Conselho Nacional de Desenvolvimento Científico e Tecnológico