Relationship between Structure and Dynamics of an Icosahedral Quasicrystal using Unsupervised Machine Learning
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arXiv
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| Format: | Preprint |
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2025
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| author | Bedolla-Montiel, Edwin A. Marín-Aguilar, Susana Dijkstra, Marjolein |
| author_facet | Bedolla-Montiel, Edwin A. Marín-Aguilar, Susana Dijkstra, Marjolein |
| contents | We present a comprehensive study of the structure, formation, and dynamics of a one-component model system that self-assembles into an icosahedral quasicrystal (IQC). Using molecular dynamics simulations combined with unsupervised machine learning techniques, we identify and characterize the unique structural motifs of IQCs, including icosahedral and dodecahedral arrangements, and quantify the evolution of local environments during the IQC formation process. Our analysis reveals that the formation of the IQC is driven by the emergence of distinct local clusters that serve as precursors to the fully developed quasicrystalline phase. Additionally, we examine the dynamics of the system across a range of temperatures, identifying transitions from vibrationally restricted motion to activated diffusion, and uncovering signatures of dynamic heterogeneity inherent to the quasicrystalline state. To directly connect structure and dynamics, we use a machine-learning-based order parameter to quantify the presence of distinct local environments across temperatures. We find that regions with high structural order, as captured by specific machine-learned classes, correlate with suppressed self-diffusion and minimal dynamical heterogeneity, consistent with phason-like motion within the IQC. In contrast, regions with lower structural order exhibit enhanced collective motion and increased dynamical heterogeneity. These results establish a quantitative framework for understanding the coupling between structural organization and dynamical processes in quasicrystals, providing new insights into the mechanisms governing IQC stability and dynamics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_15731 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Relationship between Structure and Dynamics of an Icosahedral Quasicrystal using Unsupervised Machine Learning Bedolla-Montiel, Edwin A. Marín-Aguilar, Susana Dijkstra, Marjolein Soft Condensed Matter We present a comprehensive study of the structure, formation, and dynamics of a one-component model system that self-assembles into an icosahedral quasicrystal (IQC). Using molecular dynamics simulations combined with unsupervised machine learning techniques, we identify and characterize the unique structural motifs of IQCs, including icosahedral and dodecahedral arrangements, and quantify the evolution of local environments during the IQC formation process. Our analysis reveals that the formation of the IQC is driven by the emergence of distinct local clusters that serve as precursors to the fully developed quasicrystalline phase. Additionally, we examine the dynamics of the system across a range of temperatures, identifying transitions from vibrationally restricted motion to activated diffusion, and uncovering signatures of dynamic heterogeneity inherent to the quasicrystalline state. To directly connect structure and dynamics, we use a machine-learning-based order parameter to quantify the presence of distinct local environments across temperatures. We find that regions with high structural order, as captured by specific machine-learned classes, correlate with suppressed self-diffusion and minimal dynamical heterogeneity, consistent with phason-like motion within the IQC. In contrast, regions with lower structural order exhibit enhanced collective motion and increased dynamical heterogeneity. These results establish a quantitative framework for understanding the coupling between structural organization and dynamical processes in quasicrystals, providing new insights into the mechanisms governing IQC stability and dynamics. |
| title | Relationship between Structure and Dynamics of an Icosahedral Quasicrystal using Unsupervised Machine Learning |
| topic | Soft Condensed Matter |
| url | https://arxiv.org/abs/2507.15731 |