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拓扑束缚理论通过量化原子尺度键合约束数，构建玻璃微观结构与宏观性能的定量预测模型；分子动力学模拟借助力场模型，实现从纳秒级结构弛豫到微秒级分相动力学的跨时间尺度动态演化规律；机器学习算法构建组分–性能高维映射关系，开创基于性能需求的玻璃组分逆向设计新范式。本文阐明拓扑束缚理论在预测玻璃化转变温度与硬度评估等方面中的关键作用；聚焦面向高功率器件封装、高强度及高频应用等玻璃材料分子动力学模拟研究；探讨机器学习在玻璃性能预测中的新范式，并对三者协同效应在开展新型玻璃材料研发中的应用作出展望。
Abstract：Topological constraint theory(TCT) establishes quantitative predictive models linking the microscopic structure and macroscopic properties of glass via quantifying atomic-scale bonding constraints.Molecular dynamics(MD) simulations,utilizing force field models,enable the exploration of dynamic structural evolution across temporal scales from nanosecond-level structural relaxation to microsecond-level phase separation dynamics.Machine learning(ML) algorithms construct high-dimensional composition-property mappings,thus opening a paradigm for inverse glass design based on targeted performance requirements.This review first elaborates on the fundamental principles of TCT and its pivotal role in predicting glass transition temperature,analyzing thermal expansion behavior,evaluating hardness,and uncovering the mechanisms of the mixed-alkali effect.Subsequently,it highlights the innovative research conducted by using MD simulations,i.e.,structural optimization of encapsulation glasses for high-pressure power devices,mechanical reinforcement mechanisms of high-strength glass fibers,and dielectric property modulation of glass substrates for high-frequency electronic applications as well as the applications in glass ceramics.Finally,this review discusses the emerging paradigms of ML in glass property prediction and envisions the synergistic integration of TCT,MD,and ML in the development of next-generation glass materials.Summary and Prospects TCT via quantifying the types and numbers of atomic constraints within the glass network effectively reveals the intrinsic correlation between glass structure and macroscopic properties.It provides a solid theoretical foundation for understanding and tailoring glass performance,offering a significant potential for the development of high-performance glass materials.Under the guidance of this theoretical framework,MD simulation serves as a powerful tool for investigating the atomic-scale structure and dynamic behavior of glasses,thereby offering an effective pathway to establish structure-property relationships.However,TCT is often limited to specific systems,which can introduce errors when applied to complex compositions.Meanwhile,MD simulations are computationally expensive and sometimes suffer from the absence of accurate potential functions.Several limitations still hinder their broader application i.e.,a) insufficient temporal resolution.Femtosecond-level time steps are inadequate for resolving high-frequency transient polarization responses;b) force fields often simplify quantum effects-current models,and fail to accurately describe local charge fluctuations and dynamic polarizability;and c) Limited spatial scales.Nano-sized models cannot fully capture structural heterogeneity,and statistical convergence under high-frequency electric fields is constrained by available computational power.MD simulations remain inadequate for directly investigating glass performance under high-frequency applications.To overcome these challenges,multiscale coupling models are needed,such as integrating ML algorithms to enhance the accuracy of polarization dynamics through deep learning-based potential functions,and employing materials informatics to accelerate the screening of high-performance glass compositions.These strategies are expected to significantly improve the efficiency of rational glass design.Glass-ceramics,which evolve from glasses,are widely used in applications such as encapsulation materials,printed circuit boards,microwave components,sealing glasses,and low-temperature co-fired ceramic(LTCC) substrates,having a considerable value in high-frequency communications,microelectronic packaging,and power devices.The existing research on the crystallization phenomena in glass-ceramics mainly follows two technical pathways,i.e., a) employing structural characterization methods in combination with diffusion kinetics simulations and experimental validation to indirectly infer crystal precipitation behavior,and b)constructing glass-ceramic models in MD systems by manipulation strategies such as "dig-insert" or "cut-combine" approach.However,these approaches remain inherently limited to either indirect representations of crystalline formation or manually constructed models.Overcoming the existing technological bottlenecks to enable real-time visualization of crystal nucleation and growth mechanisms during dynamic simulations remains a critical challenge.Finally,in the context of advanced packaging and heterogeneous integration,glass substrates play a crucial role in 3D integration,but face multiple challenges in interfacial reaction dynamics with silicon/metal substrates.These include atomic-scale interdiffusion leading to dielectric degradation,cross-scale coupling between nano scale chemical bond reconstruction and macroscopic stress evolution,as well as non-equilibrium thermodynamic effects induced by laser-assisted processing.There is an urgent need to develop simulation frameworks that integrate co-evolution of multiple properties across scales,enabling quantitative prediction of atomic interdiffusion coefficients,chemical bond reconstruction energy barriers,and residual stress distributions.Such efforts will provide the theoretical foundation for the design and process optimization of high-reliability glass substrate s.
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基本信息:
DOI：10.14062/j.issn.0454-5648.20250300
中图分类号:TQ171.1;TP181
引用信息:
[1]张靖,黄凯彧,杨茂源,等.拓扑束缚理论、分子动力学与机器学习在高性能玻璃材料设计中的多尺度方法解析[J].硅酸盐学报,2025,53(10):2882-2898.DOI:10.14062/j.issn.0454-5648.20250300.
基金信息:
国家重点研发计划项目(2021YFB3701600); 国家自然科学基金面上项目(52272001)