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A unified molecular level mechanism for the universal alpha- and Johari-Goldstein beta-relaxations in glassformers

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 Added by Yi-Neng Huang
 Publication date 2008
  fields Physics
and research's language is English




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We presented that the relaxation of n coupling molecules in a molecular string exhibits n individual relaxation modes (RMs), each mode being characterized by a definite relaxation time and amplitude according to the string model. The n RMs behaving a single relaxation at high temperature, evolves to two relaxation species, at low temperature, with different temperature dependences for the respective relaxation times and amplitudes. Since the characteristics of the two relaxation species are in agreement with those exhibited by the universal alpha- and Johari-Goldstein (JG) beta-relaxations in glass dynamics, we provided a unified molecular level mechanism for these two processes.



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We investigate the dielectric response in the glass-electret state of two dipolar glass-forming materials. This unusual polar glassy state of matter is produced when a dipolar liquid is supercooled under the influence of a high electric dc field, which leads to partial orientational order of the molecules carrying a dipole moment. Investigation of the prepared glass-electrets by using low-field dielectric spectroscopy reveals a clear modification of their dielectric response in the regime of the Johari-Goldstein beta-relaxation, pointing to a small but significant increase of its relaxation strength compared to the normal glass. We discuss the implications of this finding for the still controversial microscopic interpretation of the Johari-Goldstein relaxation, an inherent property of glassy matter.
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A universal supervised neural network (NN) relevant to compute the associated criticalities of real experiments studying phase transitions is constructed. The validity of the built NN is examined by applying it to calculate the criticalities of several three-dimensional (3D) models on the cubic lattice, including the classical $O(3)$ model, the 5-state ferromagnetic Potts model, and a dimerized quantum antiferromagnetic Heisenberg model. Particularly, although the considered NN is only trained one time on a one-dimensional (1D) lattice with 120 sites, yet it has successfully determined the related critical points of the studied 3D systems. Moreover, real configurations of states are not used in the testing stage. Instead, the employed configurations for the prediction are constructed on a 1D lattice of 120 sites and are based on the bulk quantities or the microscopic states of the considered models. As a result, our calculations are ultimately efficient in computation and the applications of the built NN is extremely broaden. Considering the fact that the investigated systems vary dramatically from each other, it is amazing that the combination of these two strategies in the training and the testing stages lead to a highly universal supervised neural network for learning phases and criticalities of 3D models. Based on the outcomes presented in this study, it is favorably probable that much simpler but yet elegant machine learning techniques can be constructed for fields of many-body systems other than the critical phenomena.
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