无机材料学报 ›› 2019, Vol. 34 ›› Issue (8): 885-892.DOI: 10.15541/jim20180514 CSTR: 32189.14.10.15541/jim20180514

• 研究论文 • 上一篇    下一篇

玻璃成分-结构-性质的“基因结构”模拟法

张丽艳1,李洪2,胡丽丽1,王亚杰1,3   

  1. 1. 中国科学院 上海光学精密机械研究所 高功率单元技术实验室, 上海 201815
    2. 日本电器玻璃株式会社, 谢尔比 北卡罗莱纳州, 美国 28150
    3. 中国科学院大学, 北京 100049
  • 收稿日期:2018-10-31 修回日期:2019-01-07 出版日期:2019-08-20 网络出版日期:2019-05-22
  • 作者简介:张丽艳(1971-), 女, 副研究员. E-mail: jndxzly@hotmail.com

Structure Modeling of Genes in Glass: Composition-structure-property Approach

ZHANG Li-Yan1,LI Hong2,HU Li-Li1,WANG Ya-Jie1,3   

  1. 1. Key Laboratory of Materials for High Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201815, China
    2. Nippon Electric Glass, Shelby, North Carolina 28150, USA
    3. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2018-10-31 Revised:2019-01-07 Published:2019-08-20 Online:2019-05-22

摘要:

介绍了一种基于玻璃结构性质而建立的玻璃成分(C)-结构(S)-性能(P)的统计模拟方法。分析了常用的成分-性质(C-P)模拟法的局限性以及结构-性质(S-P)模拟法的特点, 并利用磷酸盐激光钕玻璃化学稳定性改良实验比较了C-P与S-P模型的差异, 表明对于组分微调设计, 结构模拟可以给出更好的模拟结果。叙述了C-S-P模型的建模步骤, 通过模拟案例演示了使用C-S与S-P模型反演玻璃成分的具体过程。除常规性质外, C-S-P模拟法还可以对玻璃的光谱激光性质及化学性质等C-P模型难以准确模拟的性质进行预测和模拟。目的是探索一种对玻璃设计普遍适用的, 可以为新型玻璃的研发和玻璃工业生产提供高效、准确设计的便捷模拟方法。

关键词: 玻璃基因结构, 统计分析建模, 成分-结构-性质模型

Abstract:

A statistical modeling approach to modeling glass composition (C) - structure (S) - property (P) is introduced based on glass property response to the glass network structure. This paper first reviewed some of the limitations of the C-P statistical modeling approach, then followed by complementary benefit identified from using S-P statistical modeling approach. Furthermore, S-P modeling is not limited by a narrower composition space as seen in the C-P modeling case, which benefits glass composition fine-tuning and design optimization, such as in the chemical stability experiment for Nd: phosphate laser glass, the S-P models perform much better than the C-P models. The procedure of C-S-P modeling was illustrated, and how to use C-S and S-P models inverse the composition of glass was also detailed. Except for the regular properties, C-S-P modeling methodology can provide more accurate predictions on laser glass emission properties, chemical durability, etc., which are often difficult by using the C-P modeling approach alone. Our effort on C-S-P modeling is to explore a general methodology that can provide researchers with an alternative method to facilitate glass design with higher efficiency, fast turn-around, and high accuracy and precision.

Key words: glass structure gene, statistical analysis modeling, composition-structure-property model

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