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  1. Free, publicly-accessible full text available November 1, 2024
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  3. X-ray scattering has been used to characterize the columnar packing and the π stacking in a glass-forming discotic liquid crystal. In the equilibrium liquid state, the intensities of the scattering peaks for π stacking and columnar packing are proportional to each other, indicating concurrent development of the two orders. Upon cooling into the glassy state, the π–π distance shows a kinetic arrest with a change in the thermal expansion coefficient (TEC) from 321 to 109 ppm/K, while the intercolumnar spacing exhibits a constant TEC of 113 ppm/K. By changing the cooling rate, it is possible to prepare glasses with a wide range of columnar and π stacking orders, including zero order. For each glass, the columnar order and the π stacking order correspond to a much hotter liquid than its enthalpy and π–π distance, with the difference between the two internal (fictive) temperatures exceeding 100 K. By comparison with the relaxation map obtained by dielectric spectroscopy, we find that the δ mode (disk tumbling within a column) controls the columnar order and the π stacking order trapped in the glass, while the α mode (disk spinning about its axis) controls the enthalpy and the π–π spacing. Our finding is relevant for controlling the different structural features of a molecular glass to optimize its properties. 
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    Free, publicly-accessible full text available May 28, 2024
  4. The high density of aluminum nanocrystals (>10 21  m −3 ) that develop during the primary crystallization in Al-based metallic glasses indicates a high nucleation rate (∼10 18  m −3  s −1 ). Several studies have been advanced to account for the primary crystallization behavior, but none have been developed to completely describe the reaction kinetics. Recently, structural analysis by fluctuation electron microscopy has demonstrated the presence of the Al-like medium range order (MRO) regions as a spatial heterogeneity in as-spun Al 88 Y 7 Fe 5 metallic glass that is representative for the class of Al-based amorphous alloys that develop Al nanocrystals during primary crystallization. From the structural characterization, an MRO seeded nucleation configuration is established, whereby the Al nanocrystals are catalyzed by the MRO core to decrease the nucleation barrier. The MRO seeded nucleation model and the kinetic data from the delay time ( τ) measurement provide a full accounting of the evolution of the Al nanocrystal density (N v ) during the primary crystallization under isothermal annealing treatments. Moreover, the calculated values of the steady state nucleation rates ( J ss ) predicted by the nucleation model agree with the experimental results. Moreover, the model satisfies constraints on the structural, thermodynamic, and kinetic parameters, such as the critical nucleus size, the interface energy, and the volume-free energy driving force that are essential for a fully self-consistent nucleation kinetics analysis. The nucleation kinetics model can be applied more broadly to materials that are characterized by the presence of spatial heterogeneities. 
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  5. Abstract The information content of atomic-resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief among which is the column position. Neural networks (NNs) are high performance, computationally efficient methods to automatically locate atomic columns in images, which has led to a profusion of NN models and associated training datasets. We have developed a benchmark dataset of simulated and experimental STEM images and used it to evaluate the performance of two sets of recent NN models for atom location in STEM images. Both models exhibit high performance for images of varying quality from several different crystal lattices. However, there are important differences in performance as a function of image quality, and both models perform poorly for images outside the training data, such as interfaces with large difference in background intensity. Both the benchmark dataset and the models are available using the Foundry service for dissemination, discovery, and reuse of machine learning models. 
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  6. Short-timescale atomic rearrangements are fundamental to the kinetics of glasses and frequently dominated by one atom moving significantly (a rearrangement), while others relax only modestly. The rates and directions of such rearrangements (or hops) are dominated by the distributions of activation barriers ( E act ) for rearrangement for a single atom and how those distributions vary across the atoms in the system. We have used molecular dynamics simulations of Cu 50 Zr 50 metallic glass below T g in an isoconfigurational ensemble to catalog the ensemble of rearrangements from thousands of sites. The majority of atoms are strongly caged by their neighbors, but a tiny fraction has a very high propensity for rearrangement, which leads to a power-law variation in the cage-breaking probability for the atoms in the model. In addition, atoms generally have multiple accessible rearrangement vectors, each with its own E act . However, atoms with lower E act (or higher rearrangement rates) generally explored fewer possible rearrangement vectors, as the low E act path is explored far more than others. We discuss how our results influence future modeling efforts to predict the rearrangement vector of a hopping atom. 
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