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<p>In this study, data mining techniques and Molecular Dynamics simulations were used to investigate the two-phase model of liquid SiO<sub>2</sub>. Analysis of the Si–Si distance distribution revealed two distinct phases: a high-density (HD) phase and a low-density (LD) phase. The DBSCAN clustering algorithm identified spatial heterogeneities in structure and density. Further analysis showed significant differences in the intermediate-range order between the phases, while local structural investigations revealed that the HD phase forms grain-like clusters, similar to polycrystalline materials. These findings provide new insights into the structural complexity of silica, enhancing our understanding of SiO<sub>2</sub> at the atomic scale and aiding the design of silica-based materials.</p>

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Structure of Liquid SiO2 and Two-phase Model: Insight From Data Mining Techniques

  • 期刊论文

  • 发表日期：

    2025年5月19日

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  • Lan Thi Mai,
  • Dung Tri Pham,
  • Van Hong Nguyen

摘要

In this study, data mining techniques and Molecular Dynamics simulations were used to investigate the two-phase model of liquid SiO2. Analysis of the Si–Si distance distribution revealed two distinct phases: a high-density (HD) phase and a low-density (LD) phase. The DBSCAN clustering algorithm identified spatial heterogeneities in structure and density. Further analysis showed significant differences in the intermediate-range order between the phases, while local structural investigations revealed that the HD phase forms grain-like clusters, similar to polycrystalline materials. These findings provide new insights into the structural complexity of silica, enhancing our understanding of SiO2 at the atomic scale and aiding the design of silica-based materials.