Prediction of glass structure by using multiple regression analysis

Kumiko Ishii, Toru Tsuneoka, Shinichi Sakida, Yasuhiko Benino, Tokuro Nanba

Research output: Contribution to journalArticle

Abstract

Multiple regression analyses were applied to the prediction of glass structures, such as O 1s binding energy and fraction of fourfold coordinated boron atoms, N 4. In the case of linear combination of the content of glass constituents, an acceptable prediction accuracy was obtained for O 1s binding energy, and as for N 4, however, a poor agreement was observed between the prediction and measurement. After introducing quadratic and cubic interaction terms into the regression formula, a drastic improvement was achieved in the prediction of N 4. Some regression coefficients were dependent on basicity of each glass constituent, suggesting the feasibility of prediction for the glasses containing novel constituents whose regression parameters have never been determined.

Original languageEnglish
Pages (from-to)98-103
Number of pages6
JournalNippon Seramikkusu Kyokai Gakujutsu Ronbunshi/Journal of the Ceramic Society of Japan
Volume120
Issue number1399
Publication statusPublished - Mar 2012

    Fingerprint

Keywords

  • Database
  • Glass
  • NMR
  • Regression analysis
  • Structure
  • XPS

ASJC Scopus subject areas

  • Ceramics and Composites
  • Materials Chemistry
  • Chemistry(all)
  • Condensed Matter Physics

Cite this