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Simulation Methods of Glass Composition and Properties: A Short Review
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Liyan ZHANG1, Hong LI2, Shubin CHEN1, Zhongdi LI3, Minzhi RUAN3, Tianfeng XUE1, Min QIAN1, Sijun FAN1
Journal of the Chinese Ceramic Society | 2022, 50(8) : 2338 - 2350
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Journal of the Chinese Ceramic Society | 2022, 50(8): 2338-2350
• Review •
Simulation Methods of Glass Composition and Properties: A Short Review
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Liyan ZHANG1, Hong LI2, Shubin CHEN1, Zhongdi LI3, Minzhi RUAN3, Tianfeng XUE1, Min QIAN1, Sijun FAN1
Affiliations
  • 1.Key Laboratory of Materials for High Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  • 2.Nippon Electric Glass, North Carolina 28150, USA
  • 3.China Nuclear Power Engineering Co., Ltd, Beijing 100840, China
  • ZHANG Liyan (1971-), female, Associate Professor. E-mail:

About Author:

ZHANG Liyan (1971-), female, Associate Professor. E-mail:

Published: 2022-08-20 doi: 10.14062/j.issn.0454-5648.20220115
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Seven methods of modeling glass properties were briefly reviewed, i.e., covering additive method, phase diagram method, Priven method, topological theory, molecular dynamics simulation, machine learning, and mathematical statistical modeling (composition-property, structure-property, and composition-structure-property). The principle, theoretical basis, procedure of each method and their application were highlighted. The additive method demonstrates its application modeling multiple glass properties. The phase diagram approach is suitable for binary, ternary and quaternary systems of silicate, borate and borosilicate glass systems. The Priven method combines glass structure, thermodynamic equations and computer simulation; topological theory is used to simulate several properties of simple oxide and sulfide glass. The molecular dynamics simulation provides insight of molecular structures of glass of various compositions. The machine learning method is utilized based on a large database from the literature to predict properties of complex glass systems. The statistical modeling methodology is applied to bridge mathematical interrelationships of composition (C) - structure (S) - property (P) of multicomponent systems of silicate, borosilicate, and phosphate glasses. The C-S-P statistical modeling approach shows the improvement in accuracy and precision rather than the conventional C-P statistical modeling in the design of new glasses.

additive method  /  phase diagram-based method  /  priven method  /  topological theory  /  molecular dynamics simulations  /  machine learning  /  mathematical statistical modeling
张丽艳, 李洪, 陈树彬, 李忠镝, 阮苠秩, 薛天锋, 钱敏, 凡思军. 玻璃的成分和性质的模拟方法. 硅酸盐学报, 2022 , 50 (8) : 2338 -2350 . DOI: 10.14062/j.issn.0454-5648.20220115
Liyan ZHANG, Hong LI, Shubin CHEN, Zhongdi LI, Minzhi RUAN, Tianfeng XUE, Min QIAN, Sijun FAN. Simulation Methods of Glass Composition and Properties: A Short Review[J]. Journal of the Chinese Ceramic Society, 2022 , 50 (8) : 2338 -2350 . DOI: 10.14062/j.issn.0454-5648.20220115
Year 2022 volume 50 Issue 8
45
3
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Article Info
doi: 10.14062/j.issn.0454-5648.20220115
  • Receive Date:2022-02-14
  • Online Date:2026-10-20
  • Published:2022-08-20
Article Data
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History
  • Received:2022-02-14
  • Revised:2022-02-19
Funding
Affiliations
    1.Key Laboratory of Materials for High Power Laser, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
    2.Nippon Electric Glass, North Carolina 28150, USA
    3.China Nuclear Power Engineering Co., Ltd, Beijing 100840, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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