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Research Article Issue
Application of “Glass Structural Gene Modeling” in Bidirectional Design of Composition ↔ Structure↔Property
Journal of the Chinese Ceramic Society 2025, 53(10): 2755-2765
Published: 02 July 2025
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Introduction

Glass composition affects properties through structure, and this composition→structure→property relationship is one of the core topics in material science. Glass property prediction and composition design based on the simulation models become an important research interest in glass development. Glass structure gene modeling (GSgM) is a practical method to introduce the measured glass structure into simulation in a real sense. This paper was to deal with the theoretical background, model establishment, recommendation for glass design method, structural data collection as well as the limitations of GSgM. The application of GSgM in property prediction, such as physical, chemical, thermal and spectroscopic properties, and glass composition design were demonstrated in the study of laser glass and solidification glass for simulated radioactive nuclear waste.

Methods

The theoretical basis of GSgM was firstly introduced, and then the effective glass design method for the collection of simulation dataset was recommended in detail. In addition, the composition-structure (C-S), structure-property (S-P) models and the bi-directional modeling of C↔S↔P in laser glass and radioactive waste solidification glass as well as the error correction function of this method were also established.

Results and discussion

GSgM can ignore the complexity of the glass system, and all the measured glass structure data can be used for modeling. If the fine glass structure analysis (i.e., NMR or synchrotron radiation) is not available, the Raman and IR spectra are the most convenient and universal methods for glass structure characterization, in which the integrated area from the Gaussian peak fitting can be used as the structure information in the modeling. The peak-fitting is recommended to follow the characteristic vibration bands reported in references in order to decrease the relative error of the structure information. In the study of simulated radioactive waste solidification glass, The S-P modeling of chemical stability, thermal property, liquidus temperature and Mo-yellow phase models based on limited experimental data are performed to exhibit a high simulation accuracy of glass property prediction when the structure is used to do the simulation, and the model validation proves the reliability of the method. In Nd:phosphate laser glass, although the spectroscopic properties perform highly a non-linear relationship to glass composition, they can be still designed in a high accuracy via bi-directional simulation of C→S→P and P→S→C. There is another function of GSgM on error correction of experimental data. In the modeling of Tg and TL, the results show that samples with a large property measurement error can be corrected by GSgM simulation.

Conclusions

Glass structure gene modeling (GSgM) was a simulation method in which glass structure was used as a “bridge” to thread composition and property by C-S and S-P modeling. It could provide an effective prediction and optimization method for glass simulation. The high accuracy of GSgM could come from the transformation of the nonlinear relationship of C-P to two linear relationships of C-S and S-P. The GSgM approach was demonstrated as an alternative, powerful simulation method for glass modeling with limited data, and the relationship between glass composition, structure and properties could be further understood so as to achieve the more efficient simulation of glass design and glass property.

Review Issue
Simulation Methods of Glass Composition and Properties: A Short Review
Journal of the Chinese Ceramic Society 2022, 50(8): 2338-2350
Published: 01 July 2022
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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.

Research Article Issue
Statistical Structure Modeling for Thermal Properties and Chemical Stability of Borosilicate Glass Wasteforms with High Level Radioactive Waste
Journal of the Chinese Ceramic Society 2022, 50(5): 1301-1309
Published: 06 April 2022
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Statistical structure modeling can help to establish the glass formulation design models to meet the vitrification requirements of different types of high-level waste liquids due to its advantages of high efficiency and accuracy. Taking the glass transition temperature Tg, thermal expansion coefficient α and element leaching rate of Li, Na, B as target properties, we utilized a statistical structure simulation method for the development of nuclear waste glass curing formula. The results show that Tg, α and chemical stability of the glasses can be well simulated with the statistical structural data. It is also indicated that the data predicted by the model are in a reasonable agreement with the measured results at a simulation desirability 0.94. Statistical structure modeling can assist to establish the formula database of the high-level liquid waste vitrification.

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