ArticleJournal of chemical theory and computation2025
Dynamic Collision Fingerprints (DCF): Introducing a New Descriptor Linking Lattice Interactions to 2D Structural Data Signatures.
Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Chalcogen Impurity Barriers in 2D Systems via Semi-Empirical/Machine Learning Modeling: A Survey of 4000 Materials.Journal of chemical theory and computation · 2026Article
- A Comparative Study of Structural Representations for 2D Materials: Insights from Dynamic Collision Fingerprint and Matminer Library.ACS omega · 2026Article
- Interpretable Machine Learning of Nanoparticle Stability through Topological Layer Embeddings.The journal of physical chemistry. A · 2026Article
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Authors and funding
1 author.
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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.
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Registered trials
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