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Extraction of local structure differences in silica based on unsupervised learning.

Anh Khoa Augustin Lu1,2, Jianbo Lin3, Yasunori Futamura4,5,6

  • 1Research Center for Materials Nanoarchitectonics, National Institute for Materials Science, Tsukuba 305-8568, Japan. LU.Augustin@nims.go.jp.

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This study introduces an unsupervised learning method to distinguish subtle local structural differences in silica phases, crucial for understanding glass properties. The approach effectively identifies variations in atomic structures across different temperature and pressure conditions.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Condensed Matter Physics

Background:

  • Silica has a complex phase diagram with numerous structures, including glass.
  • Distinguishing between silica phases is challenging due to similar short- and medium-range order.
  • Atomistic simulations often reveal coexisting phases with subtle structural differences.

Purpose of the Study:

  • To develop a methodology for detecting subtle local structural differences among various silica phases.
  • To utilize unsupervised learning for analyzing atomic structures from molecular dynamics simulations.
  • To provide a visualization tool for understanding structural variations in silica.

Main Methods:

  • Employed unsupervised learning, specifically two-step locality preserving projections (TS-LPP).
  • Utilized locally averaged atomic fingerprints (LAAF) as a descriptor for atomic structures.
  • Generated atomic models using molecular dynamics (MD) simulations for eight silica phases.

Main Results:

  • Successfully identified a low-dimensional space where differences among silica phases are detectable.
  • Demonstrated the ability to discern subtle local structural changes and analyze phase transitions like the β-α transition in quartz.
  • Visualized structural evolution during melt-quench processes.

Conclusions:

  • The proposed methodology effectively differentiates subtle local structural variations in silica phases.
  • This approach offers a promising tool for analyzing the structure and properties of glasses synthesized under varying conditions.
  • The method enhances the understanding of structure-property relationships in amorphous materials.