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Abstract Despite decades of studies, the nature of the glass transition remains elusive. In particular, the sharpness of the dynamical arrest of a melt at the glass transition is captured by its fragility. Here, we reveal that fragility is governed by the medium-range order structure. Based on neutron-diffraction data for a series of aluminosilicate glasses, we propose a measurable structural parameter that features a strong inverse correlation with fragility, namely, the average medium-range distance (MRD). We use in-situ high-temperature neutron-scattering data to discuss the physical origin of this correlation. We argue that glasses exhibiting lowMRDvalues present an excess of small network rings. Such rings are unstable and deform more readily with changes in temperature, which tends to increase fragility. These results reveal that the sharpness of the dynamical arrest experienced by a silicate glass at the glass transition is surprisingly encoded into the stability of rings in its network. 

Abstract Machine learning (ML) is emerging as a powerful tool to predict the properties of materials, including glasses. Informing ML models with knowledge of how glass composition affects short-range atomic structure has the potential to enhance the ability of composition-property models to extrapolate accurately outside of their training sets. Here, we introduce an approach wherein statistical mechanics informs a ML model that can predict the non-linear composition-structure relations in oxide glasses. This combined model offers an improved prediction compared to models relying solely on statistical physics or machine learning individually. Specifically, we show that the combined model accurately both interpolates and extrapolates the structure of Na₂O–SiO₂glasses. Importantly, the model is able to extrapolate predictions outside its training set, which is evidenced by the fact that it is able to predict the structure of a glass series that was kept fully hidden from the model during its training. 

Abstract Chemical diversification of hybrid organic–inorganic glasses remains limited, especially compared to traditional oxide glasses, for which property tuning is possible through addition of weakly bonded modifier cations. In this work, it is shown that water can depolymerize polyhedra with labile metal–ligand bonds in a cobalt‐based coordination network, yielding a series of nonstoichiometric glasses. Calorimetric, spectroscopic, and simulation studies demonstrate that the added water molecules promote the breakage of network bonds and coordination number changes, leading to lower melting and glass transition temperatures. These structural changes modify the physical and chemical properties of the melt‐quenched glass, with strong parallels to the “modifier” concept in oxides. It is shown that this approach also applies to other transition metal‐based coordination networks, and it will thus enable diversification of hybrid glass chemistry, including nonstoichiometric glass compositions, tuning of properties, and a significant rise in the number of glass‐forming hybrid systems by allowing them to melt before thermal decomposition. 

Abstract Topological constraint theory (TCT) has enabled the prediction of various properties of oxide glasses as a function of their composition and structure. However, the robust application of TCT relies on accurate knowledge of the network structure and topology. Here, based on classical molecular dynamics simulations, we derive a fully analytical model describing the topology of the calcium aluminosilicate [(CaO)_x(Al₂O₃)_y(SiO₂)_1−x−y, CAS] ternary system. This model yields the state of rigidity (flexible, isostatic, or stressed‐rigid) of CAS systems as a function of composition and temperature. These results reveal the existence of correlations between network topology and glass‐forming ability. This study suggests that glass‐forming ability is encoded in the network topology of the liquid state rather than that of the glassy state. 

A graph-based machine learning model is built to predict atom dynamics from their static structure, which, in turn, unveils the predictive power of static structure in dynamical evolution of disordered phases.