Article Dans Une Revue The Journal of Chemical Physics Année : 2025

Comparing dimensionality reduction methods for local structural identification in colloidal systems

Résumé

Quantifying local structures in self-assembled systems is a central challenge in soft matter and materials science. When no a priori knowledge of the relevant structures is available, traditional order parameters often fall short. Unsupervised machine learning provides a convenient route to autonomously uncover structural motifs directly from particle configurations. In this work, we systematically compare three popular dimensionality reduction techniques; Principal Component Analysis (PCA), Autoencoders (AE), and Uniform Manifold Approximation and Projection (UMAP), for classifying local environments in self-assembled systems. We first apply these methods to fluid and crystal configurations of hard and charged spheres. Thereafter, we apply it to an icosahedral arrangement of spheres that self-assembled in spherical confinement, both from simulations as well as from experiments. We demonstrate that UMAP consistently outperforms the other methods in capturing complex structural features, offering a robust tool for structural classification without supervision.

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hal-05504774 , version 1 (11-02-2026)

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A. Ulugöl, J I Bückmann, R. Yang, L D Hoitink, A. van Blaaderen, et al.. Comparing dimensionality reduction methods for local structural identification in colloidal systems. The Journal of Chemical Physics, 2025, 164 (6), pp.064107. ⟨10.1063/5.0302107⟩. ⟨hal-05504774⟩
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