An Avramov-based viscosity model for the SiO2-Al2O3-Na2O-K2O system in a wide temperature range

On Viscous Flow in Glass-Forming Organic Liquids

“…The Sheffield equation of viscosity provides data on chemical bond parameters, including both enthalpies and entropies. Recently developed artificial intelligence and machine learning techniques (see [16,19,57]) can effectively give detailed information on the bonding system of materials, thereby contributing to efforts to improve the properties and functionalities of novel glasses.…”

“…Comprehensive comparisons of viscosity models available for a number of oxide glasses were provided by Starodub et al [16], Sturm [17], and Chen et al [19]. Starodub et al [16] demonstrated that the outcomes of modeling can be effectively improved by using machine learning techniques applied to multiparameter tasks.…”

“…Comprehensive comparisons of viscosity models available for a number of oxide glasses were provided by Starodub et al [16], Sturm [17], and Chen et al [19]. Starodub et al [16] demonstrated that the outcomes of modeling can be effectively improved by using machine learning techniques applied to multiparameter tasks. Chen et al [19] found that the viscosity values simulated by using both high-temperature and low-temperature viscosity data show higher accuracy than those using only high-temperature viscosity data.…”

“…The salient feature characterizing a supercooled liquid is the dramatic increase of viscosity η(T) with decreasing temperature T, which may encompass some 15 orders of magnitude over a temperature range of almost several hundred K [1][2][3][4][5][6][7]. The interest in analyzing the viscous flow in glass-forming materials is not diminishing, with many novel findings having occurred over the last decade [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. There are many theoretical models that can describe the viscous flow of glass-forming materials, which provide reasonably exact descriptions of viscosity-temperature relationships .…”

Structure and properties of alkali aluminosilicate glasses and melts: Insights from deep learning

“…Their model connects viscosity, heat capacity, configurational entropy, structure and chemical composition for melts in the Na 2 O-K 2 O-SiO 2 system and allows viscosity predictions with an average error lower than 0.2 log 10 Pa•s in the 10 0 -10 12 Pa•s range. A third example is the model proposed by Starodub et al (2019). It combines an associated solution model of melt structure with the Avramov-Milchev equation of viscous flow (Avramov and Milchev, 1988;Avramov, 2011) to predict melt viscosity in the 10 0 -10 12 Pa•s range in the Na 2 O-K 2 O-Al 2 O 3 -SiO 2 system.…”