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Relevant bibliographies by topics / Polymorphism - Network Forming Liquids

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Academic literature on the topic 'Polymorphism - Network Forming Liquids'

Author: Grafiati

Published: 7 September 2023

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Journal articles on the topic "Polymorphism - Network Forming Liquids"

1

Hernandes, V. F., M. S. Marques, and José Rafael Bordin. "Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures." Journal of Physics: Condensed Matter 34, no. 2 (2021): 024002. http://dx.doi.org/10.1088/1361-648x/ac2f0f.

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Abstract Characterization of phases of soft matter systems is a challenge faced in many physical chemical problems. For polymorphic fluids it is an even greater challenge. Specifically, glass forming fluids, as water, can have, besides solid polymorphism, more than one liquid and glassy phases, and even a liquid–liquid critical point. In this sense, we apply a neural network algorithm to analyze the phase behavior of a mixture of core-softened fluids that interact through the continuous-shouldered well (CSW) potential, which have liquid polymorphism and liquid–liquid critical points, similar to water. We also apply the neural network to mixtures of CSW fluids and core-softened alcohols models. We combine and expand methods based on bond-orientational order parameters to study mixtures, applied to mixtures of hardcore fluids and to supercooled water, to include longer range coordination shells. With this, the trained neural network was able to properly predict the crystalline solid phases, the fluid phases and the amorphous phase for the pure CSW and CSW-alcohols mixtures with high efficiency. More than this, information about the phase populations, obtained from the network approach, can help verify if the phase transition is continuous or discontinuous, and also to interpret how the metastable amorphous region spreads along the stable high density fluid phase. These findings help to understand the behavior of supercooled polymorphic fluids and extend the comprehension of how amphiphilic solutes affect the phases behavior.

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2

Jin, Yi, Aixi Zhang, Sarah E. Wolf, et al. "Glasses denser than the supercooled liquid." Proceedings of the National Academy of Sciences 118, no. 31 (2021): e2100738118. http://dx.doi.org/10.1073/pnas.2100738118.

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When aged below the glass transition temperature, Tg, the density of a glass cannot exceed that of the metastable supercooled liquid (SCL) state, unless crystals are nucleated. The only exception is when another polyamorphic SCL state exists, with a density higher than that of the ordinary SCL. Experimentally, such polyamorphic states and their corresponding liquid–liquid phase transitions have only been observed in network-forming systems or those with polymorphic crystalline states. In otherwise simple liquids, such phase transitions have not been observed, either in aged or vapor-deposited stable glasses, even near the Kauzmann temperature. Here, we report that the density of thin vapor-deposited films of N,N′-bis(3-methylphenyl)-N,N′-diphenylbenzidine (TPD) can exceed their corresponding SCL density by as much as 3.5% and can even exceed the crystal density under certain deposition conditions. We identify a previously unidentified high-density supercooled liquid (HD-SCL) phase with a liquid–liquid phase transition temperature (TLL) ∼35 K below the nominal glass transition temperature of the ordinary SCL. The HD-SCL state is observed in glasses deposited in the thickness range of 25 to 55 nm, where thin films of the ordinary SCL have exceptionally enhanced surface mobility with large mobility gradients. The enhanced mobility enables vapor-deposited thin films to overcome kinetic barriers for relaxation and access the HD-SCL state. The HD-SCL state is only thermodynamically favored in thin films and transforms rapidly to the ordinary SCL when the vapor deposition is continued to form films with thicknesses more than 60 nm.

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3

Balyakin, I. A., R. E. Ryltsev, and N. M. Chtchelkatchev. "Liquid–Crystal Structure Inheritance in Machine Learning Potentials for Network-Forming Systems." JETP Letters 117, no. 5 (2023): 370–76. http://dx.doi.org/10.1134/s0021364023600234.

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It has been studied whether machine learning interatomic potentials parameterized with only disordered configurations corresponding to liquid can describe the properties of crystalline phases and predict their structure. The study has been performed for a network-forming system SiO2, which has numerous polymorphic phases significantly different in structure and density. Using only high-temperature disordered configurations, a machine learning interatomic potential based on artificial neural networks (DeePMD model) has been parameterized. The potential reproduces well ab initio dependences of the energy on the volume and the vibrational density of states for all considered tetra- and octahedral crystalline phases of SiO2. Furthermore, the combination of the evolutionary algorithm and the developed DeePMD potential has made it possible to reproduce the really observed crystalline structures of SiO2. Such a good liquid–crystal portability of the machine learning interatomic potential opens prospects for the simulation of the structure and properties of new systems for which experimental information on crystalline phases is absent.

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4

Takéuchi, Yasushi. "Hydrodynamic Scaling and the Intermediate-Range Order in Network-Forming Liquids." Progress of Theoretical Physics Supplement 178 (2009): 181–86. http://dx.doi.org/10.1143/ptps.178.181.

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5

Hong, N. V., N. V. Huy, and P. K. Hung. "The structure and dynamic in network forming liquids: molecular dynamic simulation." International Journal of Computational Materials Science and Surface Engineering 5, no. 1 (2012): 55. http://dx.doi.org/10.1504/ijcmsse.2012.049058.

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6

Yang, Ke, Zhikun Cai, Madhusudan Tyagi, et al. "Odd–Even Structural Sensitivity on Dynamics in Network-Forming Ionic Liquids." Chemistry of Materials 28, no. 9 (2016): 3227–33. http://dx.doi.org/10.1021/acs.chemmater.6b01429.

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7

Liu, Mengtan, Ryan D. McGillicuddy, Hung Vuong, et al. "Network-Forming Liquids from Metal–Bis(acetamide) Frameworks with Low Melting Temperatures." Journal of the American Chemical Society 143, no. 7 (2021): 2801–11. http://dx.doi.org/10.1021/jacs.0c11718.

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8

Zhu, W., Y. Xia, B. G. Aitken, and S. Sen. "Temperature dependent onset of shear thinning in supercooled glass-forming network liquids." Journal of Chemical Physics 154, no. 9 (2021): 094507. http://dx.doi.org/10.1063/5.0039798.

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9

Hong, N. V., N. V. Huy, and P. K. Hung. "The correlation between coordination and bond angle distribution in network-forming liquids." Materials Science-Poland 30, no. 2 (2012): 121–30. http://dx.doi.org/10.2478/s13536-012-0019-y.

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10

Maruyama, Kenji, Hirohisa Endo, and Hideoki Hoshino. "Voids and Intermediate-Range Order in Network-Forming Liquids: Rb20Se80 and BiBr3." Journal of the Physical Society of Japan 76, no. 7 (2007): 074601. http://dx.doi.org/10.1143/jpsj.76.074601.

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