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The fragility of a glassforming liquid characterizes how rapidly itsrelaxation dynamics slow down with cooling. The viscosity of strong liquidsfollows an Arrhenius law with a temperature-independent barrier height torearrangements responsible for relaxation, whereas fragile liquids experience amuch faster increase in their dynamics, suggesting a barrier height thatincreases with decreasing temperature. Strong glassformers are typicallynetwork glasses, while fragile glassformers are typically molecular orhard-sphere-like. As a result of these differences at the microscopic level,strong and fragile glassformers are usually treated separately from atheoretical point of view. Silica is the archetypal strong glassformer at lowtemperatures, but also exhibits a mysterious strong-to-fragile crossover athigher temperatures. Here we show that softness, a structure-based machinelearned parameter that has previously been applied to fragile glassformersprovides a useful description of model liquid silica in the strong and fragileregimes, and through the strong-to-fragile crossover. Just as for fragileglassformers, the relationship between softness and dynamics is invariant andArrhenius in all regimes, but the average softness changes with temperature.The strong-to-fragile crossover in silica is not due to a sudden, qualitativechange in structure, but can be explained by a simple Arrhenius form with acontinuously and linearly changing local structure. Our results unify the studyof liquid silica under a single simple conceptual picture.

Unifying framework for strong and fragile liquids via machine learning: a study of liquid silica