Is there a one-to-one correspondence between interparticle interactions and physical properties of liquid?

Мокшин, А. В.; Khabibullin, R A

doi:10.1016/j.physa.2022.128297

Cited by 4 publications

(3 citation statements)

References 62 publications

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“…Therefore, obtaining an analytical expression that allows one to determine

based on the known key physical characteristics of glass-forming liquids remains an unsolved task. It is obvious that the correct solution of this task is possible using machine learning methods, which will allow us to reveal hidden relationships between physical characteristics and determine the most significant factors in estimating

[ 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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

2023

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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.

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“…Therefore, obtaining an analytical expression that allows one to determine

[ 22 , 23 , 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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

2023

Self Cite

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“…This makes the process of synthesizing new alloys extremely difficult and significantly increases the costs. Furthermore, methods of computer design seem to be a suitable support for empirical methods at the stage of determining amorphous metal alloys with desired mechanical properties [27,28]. 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].…”

Section: Introductionmentioning

confidence: 99%

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

Galimzyanov

Doronina

Мокшин

2023

Metals

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The development and implementation of the methods for designing amorphous metal alloys with desired mechanical properties is one of the most promising areas of modern materials science. Here, the machine learning methods appear to be a suitable complement to empirical methods related to the synthesis and testing of amorphous alloys of various compositions. In the present work, it is proposed a method to determine amorphous metal alloys with mechanical properties closest to those required. More than 50,000 amorphous alloys of different compositions have been considered, and the Young’s modulus E and the yield strength σy have been evaluated for them by the machine learning model trained on the fundamental physical properties of the chemical elements. Statistical treatment of the obtained results reveals that the fundamental physical properties of the chemical element with the largest mass fraction are the most significant factors, whose values correlate with the values of the mechanical properties of the alloys, in which this element is involved. It is shown that the values of the Young’s modulus E and the yield strength σy are higher for amorphous alloys based on Cr, Fe, Co, Ni, Nb, Mo and W formed by the addition of semimetals (e.g., Be, B, Al, Sn), nonmetals (e.g., Si and P) and lanthanides (e.g., La and Gd) than for alloys of other compositions. Increasing the number of components in alloy from 2 to 7 and changing the mass fraction of chemical elements has no significantly impact on the strength characteristics E and σy. Amorphous metal alloys with the most improved mechanical properties have been identified. In particular, such extremely high-strength alloys include Cr80B20 (among binary), Mo60B20W20 (among ternary) and Cr40B20Nb10Pd10Ta10Si10 (among multicomponent).

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“…It is obvious that the correct solution of this task is possible using machine learning methods, which will allow us to reveal hidden relationships between physical characteristics and determine the most significant factors in estimating T A [22,23,24,25,26].…”

Section: Introductionmentioning

confidence: 99%

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

Galimzyanov,

Doronina,

Mokshin

2023

Preprint

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The Arrhenius crossover temperature, T A , 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 T A . 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 T A and the physical properties of liquids directly related to their glass-forming ability. Using a machine learning model, the crossover temperature T A was calculated for silicates, borates, organic compounds and metal melts of various compositions. The empirical values of the glass transition temperature T g , the melting temperature T m , the ratio of these temperatures T g /T m and the fragility index m were applied as input parameters. It has been established that the temperatures T g and T m are significant parameters, whereas their ratio T g /T m and the fragility index m do not correlate much with the temperature T A . An important result of the present work is the analytical equation relating the temperatures T g , T m and T A , 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 T A for a large class of materials, regardless of their compositions and glass-forming abilities.

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Is there a one-to-one correspondence between interparticle interactions and physical properties of liquid?

Cited by 4 publications

References 62 publications

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

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

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

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

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