定量结构-性质关系分析法在玻璃材料领域的研究进展

doi:10.16552/j.cnki.issn1001-1625.2025.1143

摘要/Abstract

摘要：

复杂的非晶态结构和热力学亚稳态特征导致研究人员对玻璃材料结构-性能映射关系的理解仍面临挑战，因此，构效关系研究成为新型高性能玻璃研制的核心难题之一。定量结构-性质关系（QSPR）分析法通过建立数学模型将材料的微观结构与宏观性能相关联，为新型玻璃材料的研究提供了可以突破传统试错法局限的新途径。本文系统综述了QSPR分析法在玻璃材料领域中的最新研究进展：首先，梳理了从数据获取、描述符提取到模型构建的完整研究流程；其次，聚焦描述符体系的分类，阐述了自该方法首次提出后描述符体系在结构、能量和动力学机制方面的发展演化；随后，进一步总结了QSPR分析法在硅酸盐玻璃、磷酸盐玻璃及其他类型玻璃中成功预测力学、热学及化学稳定性等性能的案例，论证了其在实际材料设计中的有效性。最后分析了当前研究在描述符物理意义与数据质量方面面临的挑战，并展望了未来通过多尺度融合与多方法联用的改进策略，以推动QSPR分析法从解释工具向精准设计平台的转变。

关键词: 定量结构-性质关系, 玻璃材料, 结构描述符, 分子动力学模拟, 性能预测, 材料设计

Abstract:

The complex amorphous structure and metastable thermodynamic characteristics of glass materials pose challenges to understanding the mapping relationship between the structure and the performance of this kind of materials. Therefore， the structure-properties relationship becomes one of the core challenges in developing new high-performance glass. The quantitative structure-property relationship （QSPR） analysis method， which can establish a mathematical model to link the microscopic structure with macroscopic properties， provides a new approach to break through the limitation of traditional trial-and-error methods in designing new glass materials. This paper systematically reviews the latest progresses of QSPR analysis method in the field of glass materials. Firstly， it sorts out the complete process of the QSPR analysis method， including the data acquisition， the descriptor extraction， and the model construction. Secondly， it focuses on the classification of descriptor systems and elaborates on the development and evolution of descriptor systems in terms of structure， energy and dynamic mechanisms. Then， successful prediction cases using QSPR analysis method are further summarized， including mechanical properties， thermal properties and chemical durability in silicate， phosphate， and other glass systems， demonstrating its ability in practical material design. Finally， current challenges in understanding the physical meaning of descriptors and improving the data quality are introduced， and outlook of the future improving strategy by integrating multi-scale information and combining multi-methods are proposed， in order to promote the QSPR analysis method from an explanatory tool into a precise design platform.

Key words: quantitative structure-property relationship, glass material, structural descriptor, molecular dynamics simulation, property prediction, material design

中图分类号:

TQ171

闫洪滢, 严静萍, 蒋芳玲, 郑秋菊, 邓路. 定量结构-性质关系分析法在玻璃材料领域的研究进展[J]. 硅酸盐通报, 2026, 45(3): 755-770.

YAN Hongying, YAN Jingping, JIANG Fangling, ZHENG Qiuju, DENG Lu. Research Progress of Quantitative Structure-Property Relationship Analysis Method in the Field of Glass Materials[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2026, 45(3): 755-770.

图/表 9

图1 QSPR分析法在玻璃材料领域发展历程

Fig.1 Development history of QSPR analysis method in the field of glass materials

图2 玻璃材料QSPR分析法研究流程

Fig.2 Research process of QSPR analysis method for glass materials

图3 实验测量的密度（a）、硬度（b）、玻璃化转变温度（c），以及使用配位数为结构输入，探究用于评估玻璃网络整体强度的结构描述符Fnet与密度（d）、硬度（e）、玻璃化转变温度（f）的相关性，以及使用Q n 为结构输入，探究Fnet与密度（g）、硬度（h）、玻璃化转变温度（i）的相关性［43］

Fig.3 Experimentally measured density （a）， hardness （b）， glass transition temperature （c） and use coordination number as structure input to explore correlation between structural descriptor Fnet， which is used to evaluate the overall strength of glass network， and density （d）， hardness （e）， glass transition temperature （f）， and use Q n as structural input to explore correlation between Fnet and density （g）， hardness （h）， glass transition temperature （i）［43］

图4 QSPR建模的工作流程［50］

Fig.4 Workflow of QSPR modeling［50］

表1 Fnet描述符对于密度、玻璃化转变温度、热膨胀系数、杨氏模量和硬度的R2的值［14］

Table 1 Fnet descriptor provides values R2 for density， glass transition temperature， CTE， Young’s modulus and hardness［14］

Item	F_net	R²
Density	BSdc/T_m with M_NC	0.865
	BSdc with M_NC	0.838
	SBS/T_m	0.813
T_g	EF/T_mwith M_NC	0.732
T_g	EF with M_NC	0.711
CTE	SBS with M_NC	0.973
	SBS/T_m with M_NC	0.972
	BSdm with M_NC	0.971
	BSdm/T_m with M_NC	0.943
Young’s modulus	EF/T_m	0.988
	EF/T_m with M_NC	0.953
	EF	0.987
	EF with M_NC	0.958
Hardness	EF	0.933
	EF/T_m	0.925
	EF/T_m with M_NC	0.844

表2 SAP系列与硅酸盐玻璃中不同性质相关的Ccoe系数［43］

Table 2 Ccoe coefficients associated with different properties in SAP series and silicate glass［43］

Structure	Density	Hardness	T_g	CTE	Young’s modulus	η_P	η_Al	η_Si
CN	1	1/R'_M	R_Al	1/R'_M	1	1/R'_p	R $A l'$	R $S i'$
Q ⁿ	R'_M	1/R'_M	1	1/R'_M	1	1/R'_p	R $A l'$	R $S i'$

表2 SAP系列与硅酸盐玻璃中不同性质相关的Ccoe系数［43］

Table 2 Ccoe coefficients associated with different properties in SAP series and silicate glass［43］

Structure	Density	Hardness	T_g	CTE	Young’s modulus	η_P	η_Al	η_Si
CN	1	1/R'_M	R_Al	1/R'_M	1	1/R'_p	R $A l'$	R $S i'$
Q ⁿ	R'_M	1/R'_M	1	1/R'_M	1	1/R'_p	R $A l'$	R $S i'$

表3 QSPR在玻璃材料领域的混合型描述符

Table 3 QSPR hybrid descriptors in the field of glass materials

Development stage	Descriptor	Key improvement	Advantage	Limitation
Early model	F_net-BE^［31］	Combination of CN and BE	Pioneering the fusion of energy and structural parameters.	Dependent on MD simulation， limited application scope
Energy-related developments	F_net-SBS^［13］	Optimized energy parameters： Replace BE with SBS and introduce M_NC	Parameter optimization， significantly enhanced correlation	Dependent on experimental bond energy data
	F_net-FE^［55］	Substitution of EF with formation energy	Good correlation in silicate systems， high prediction accuracy	Strong dependency on FE data， high computational cost
	F_net-CSE^［43］	Using electronegativity to replace bond energy， C_coe is introduced	No experimental input required， strong cross-system applicability	Al CN prediction deviation， neglect of covalent effects
	Average metal oxide dissociation energy^［33］	Integration of metal-oxygen bond strengths and CN	Direct-correlation with dissolution energy barrier， excellent prediction performance	Strong dependency on CN accuracy
Structure-related developments	Three-scale F_net^［16］	Based on multi-scale structural descriptor： CN， Q ⁿ， and ring size	Validation of multi-scale QSPR strategy	Dependent on MD simulation， high computational cost
Structure-related developments	Topological indices descriptor^［60］	Quantification of spatial connectivity complexity in silicate networks	Highly correlated with various properties	Sensitive to methods of model construction
Kinetics-related developments	F_net-variants^［14］	Replace the fixed value m_ij by the NC-based dynamic factor	Identified optimal descriptor forms for different properties	Relatively complex parameters
Kinetics-related developments	Average self-diffusion coefficient of the molten state^［33］	Independent of bond energy parameters	Insensitive to thermal history， good stability	Neglect of external factors such as interface reactions

表3 QSPR在玻璃材料领域的混合型描述符

Table 3 QSPR hybrid descriptors in the field of glass materials

Development stage	Descriptor	Key improvement	Advantage	Limitation
Early model	F_net-BE^［31］	Combination of CN and BE	Pioneering the fusion of energy and structural parameters.	Dependent on MD simulation， limited application scope
Energy-related developments	F_net-SBS^［13］	Optimized energy parameters： Replace BE with SBS and introduce M_NC	Parameter optimization， significantly enhanced correlation	Dependent on experimental bond energy data
	F_net-FE^［55］	Substitution of EF with formation energy	Good correlation in silicate systems， high prediction accuracy	Strong dependency on FE data， high computational cost
	F_net-CSE^［43］	Using electronegativity to replace bond energy， C_coe is introduced	No experimental input required， strong cross-system applicability	Al CN prediction deviation， neglect of covalent effects
	Average metal oxide dissociation energy^［33］	Integration of metal-oxygen bond strengths and CN	Direct-correlation with dissolution energy barrier， excellent prediction performance	Strong dependency on CN accuracy
Structure-related developments	Three-scale F_net^［16］	Based on multi-scale structural descriptor： CN， Q ⁿ， and ring size	Validation of multi-scale QSPR strategy	Dependent on MD simulation， high computational cost
Structure-related developments	Topological indices descriptor^［60］	Quantification of spatial connectivity complexity in silicate networks	Highly correlated with various properties	Sensitive to methods of model construction
Kinetics-related developments	F_net-variants^［14］	Replace the fixed value m_ij by the NC-based dynamic factor	Identified optimal descriptor forms for different properties	Relatively complex parameters
Kinetics-related developments	Average self-diffusion coefficient of the molten state^［33］	Independent of bond energy parameters	Insensitive to thermal history， good stability	Neglect of external factors such as interface reactions

图5 Fnet描述符分别与实验密度（a）、玻璃化转变温度（b）、热膨胀系数（c）、杨氏模量（d）、硬度（e）的相关性［14］

Fig.5 Relationship between Fnet descriptor and experimental density （a）， glass transition temperature （b）， CTE （c）， Young’s modulus （d）， hardness （e）［14］

图6 初始溶解速率r0与Fnet（a）、Fnet 和网络连接性修正因子（b）、桥氧百分比（c）、整体网络连接性（d）的相关性［50］

Fig.6 Correlation between initial dissolution rate r0 and Fnet（a）， Fnet and modified network connectivity （b）， bridge oxygen percentage （c）， overall network connectivity （d）［50］

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