Bonding and the dynamics of glassy network liquids
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912503989010432 |
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| author | Brown, M. H. Wolynes, P. G. |
| author_facet | Brown, M. H. Wolynes, P. G. |
| contents | The Random First Order Transition (RFOT) theory of glasses provides a unified framework for explaining the observed correlations of the kinetic and thermodynamic behaviors of glass-forming liquids having a wide variety of chemical compositions and interactions. The theory also provides a solid starting point for calculating glassy dynamics starting from the microscopic forces. Network liquids, which interact via long-lived, geometrically constraining interactions, such as covalent bonding, have competing energy scales for bond breaking events and for collective particle rearrangement events. In this paper, we show microscopic calculations via the RFOT theory can predict how glassy dynamics depends on the degree of bonding, focusing on mixtures of network-forming particles with non-bonding impurities as in familiar window glass. By introducing soft-core nonbonding interactions, we show the viscosity and fragility of the network liquid model can be computed as a function of composition, temperature, and density or pressure. We find that the fragility in the strong-bond limit depends only on composition and not on the bond breaking energy and describes well corresponding measurements in sodium or potassium silicates. The model predicts that materials with weaker bonds may show a non-monotonic trend in the fragility as a function of composition. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_19676 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bonding and the dynamics of glassy network liquids Brown, M. H. Wolynes, P. G. Soft Condensed Matter Disordered Systems and Neural Networks The Random First Order Transition (RFOT) theory of glasses provides a unified framework for explaining the observed correlations of the kinetic and thermodynamic behaviors of glass-forming liquids having a wide variety of chemical compositions and interactions. The theory also provides a solid starting point for calculating glassy dynamics starting from the microscopic forces. Network liquids, which interact via long-lived, geometrically constraining interactions, such as covalent bonding, have competing energy scales for bond breaking events and for collective particle rearrangement events. In this paper, we show microscopic calculations via the RFOT theory can predict how glassy dynamics depends on the degree of bonding, focusing on mixtures of network-forming particles with non-bonding impurities as in familiar window glass. By introducing soft-core nonbonding interactions, we show the viscosity and fragility of the network liquid model can be computed as a function of composition, temperature, and density or pressure. We find that the fragility in the strong-bond limit depends only on composition and not on the bond breaking energy and describes well corresponding measurements in sodium or potassium silicates. The model predicts that materials with weaker bonds may show a non-monotonic trend in the fragility as a function of composition. |
| title | Bonding and the dynamics of glassy network liquids |
| topic | Soft Condensed Matter Disordered Systems and Neural Networks |
| url | https://arxiv.org/abs/2507.19676 |