Previsão da habilidade de formar vidros (gfa) via algoritmos de inteligência artificial
Abstract
Glasses are often formed through the supercooling of liquids. The ability to form glasses,
known as glass-forming ability (GFA), is a fundamental property in the development of
new glasses; however, obtaining it through experimental methods is costly. Therefore,
there is significant scientific interest in the development of indirect methods for GFA
acquisition. Previous studies have identified two fundamental parameters for predicting
GFA: the liquidus temperature and the viscosity at the liquidus temperature. In this
work, viscosity at Tliquidus was determined for 4950 oxide glass compositions by fitting the
Vogel–Fulcher–Tammann–Hesse equation (VFTH, log10 η(T)=A+B/(T- T0), where η is
in Pa.s and T is in K).
This work proposes the integration of physical models and Machine Learning (ML)
to predict the GFA of oxide glasses through two approaches. In the indirect pathway, an
ML model is generated to predict the Tliquidus, and another model predicts the viscosity
at Tliquidus calculated by the VFTH model. Subsequently, these two parameters are used
to calculate GFA through the "Jezica"physical model, where GFA = η(Tl) / Tl
2
. The
influence of each component in predicting viscosity was evaluated based on the roles
of network-forming, intermediate, and modifier oxides, providing a new perspective in
structural theories and their role in properties. On the other hand, in the direct pathway,
Tliquidus and viscosity at Tliquidus are used to calculate GFA, and the ML model allows for
the direct prediction of GFA. These approaches aim to enable the prediction of GFA solely
based on the composition of oxide glass formers and to enhance the understanding of this
property, thereby accelerating the development of new glasses.
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