Download file (2.56 MB)
Download as zip (2.3 MB)

Collection

Citation

Description:

Silica exhibits a rich phase diagram with numerous stable structures existing at different temperature and pressure conditions, including its glassy form. In large-scale atomistic simulations, due to the small energy difference, several phases may coexist. While, in terms of long-range order, there are clear differences between these phases, their short- or medium-range structural properties are similar for many phases, thus making it difficult to detect the structural differences.

In this study, a methodology based on unsupervised learning is proposed to detect the differences in local structures between eight phases of silica, using atomic models prepared by molecular dynamics (MD) simulations. A combination of two-step locality preserving projections (TS-LPP) and locally averaged atomic fingerprints (LAAF) descriptor was employed to and a low-dimensional space in which the differences among all the phases can be detected. From the distance between each structure in the found low-dimensional space, the similarity between the structures can be discussed and subtle local changes in the structures can be detected.

Using the obtained low-dimensional space, the β −α transition in quartz at a low temperature was analyzed, as well as the structural evolution during the melt-quench process starting from α-quartz. The proper differentiation and ease of visualization make the present methodology promising for improving the analysis of the structure
and properties of glasses, where subtle difference in structure appear due to differences in the
temperature and pressure conditions at which they were synthesized.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: silica, unsupervised learning, Machine Learning, Molecular Dynamics(MD), Density Functional Theory (DFT), local structure analysis

Date published: 2024-03-28

Publisher: Royal Society of Chemistry (RSC)

Journal:

• Physical Chemistry Chemical Physics (ISSN: 14639084) p. 11657-11666

Funding:

• Japan Society for the Promotion of Science 20H05883
• Japan Society for the Promotion of Science 18H01143
• Japan Society for the Promotion of Science 21H01008

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1039/d3cp06298h

Related item:

Other identifier(s):

Contact agent:

Updated at: 2024-08-20 08:30:29 +0900

Published on MDR: 2024-08-20 08:30:29 +0900