Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network

Abstract

The Arrhenius crossover temperature, TA, corresponds to a thermodynamic state wherein the atomistic dynamics of a liquid becomes heterogeneous and cooperative; and the activation barrier of diffusion dynamics becomes temperature-dependent at temperatures below TA. The theoretical estimation of this temperature is difficult for some types of materials, especially silicates and borates. In these materials, self-diffusion as a function of the temperature T is reproduced by the Arrhenius law, where the activation barrier practically independent on the temperature T. The purpose of the present work was to establish the relationship between the Arrhenius crossover temperature TA and the physical properties of liquids directly related to their glass-forming ability. Using a machine learning model, the crossover temperature TA was calculated for silicates, borates, organic compounds and metal melts of various compositions. The empirical values of the glass transition temperature Tg, the melting temperature Tm, the ratio of these temperatures Tg/Tm and the fragility index m were applied as input parameters. It has been established that the temperatures Tg and Tm are significant parameters, whereas their ratio Tg/Tm and the fragility index m do not correlate much with the temperature TA. An important result of the present work is the analytical equation relating the temperatures Tg, Tm and TA, and that, from the algebraic point of view, is the equation for a second-order curved surface. It was shown that this equation allows one to correctly estimate the temperature TA for a large class of materials, regardless of their compositions and glass-forming abilities.

The Activation Energy Temperature Dependence for Viscous Flow of Chalcogenides

“…The crossover temperature T A is assumed to be close to the liquidus temperature T liq , at which the material is completely melted [5]. It was recently shown [7] that regardless of the type of glass-forming liquid, the crossover temperature is given by the universal Equation (2):…”

“…where T m is the melting temperature and k = 1.1 ± 0.15 (see for details Figure 3b of reference [7]). In addition, the T A of certain glass families, such as float and nuclear waste glasses, can be defined using a fixed viscosity value which is independent of composition [8].…”

Neural Network as a Tool for Design of Amorphous Metal Alloys with Desired Elastoplastic Properties

“…In recent decades, rapid development of information technologies as well as automation of data collection and storage processes have contributed to the accumulation and systematization of information about the physical and mechanical properties of bulk amorphous metal alloys glasses [29][30][31][32]. The methods of machine learning operate with large arrays of the data and allow us to determine the relationship between composition and properties of alloys both already known and not previously known [33][34][35][36]. For example, Xiong and co-authors have been developed a machine learning model that can predict the glass-forming ability and elastic moduli of bulk metallic glasses based on the fundamental atomic properties, chemical and physical properties obtained from experiments or density functional theory simulations [37].…”

“…and the database Materials Project [46] as well as from Refs [36,[47][48][49][50]. [see Supplementary data of the present work].…”

“…The training subset consists of amorphous metal alloys based on Al, Au, Ca, Co, Cu, Fe, La, Hf, etc. with different compositions, for which the values of E and σ y are known[36,[47][48][49][50]. The physical properties of the chemical elements of these alloys are also used in the training procedure [see Supplementary Data].…”