Quasi-two-dimensional strong liquid-like dynamics of surface atoms in metallic glasses

The fast dynamic properties of the surface of metallic glasses(MGs) play a critical role in determining their potential applications. However, due to the significant difference in thermal history between atomic simulation models and laboratory-made samples, the atomic-scale behaviors of the fast surface dynamics of MGs in experiments remain uncertain. Herein, we prepared model MG films with notable variations in thermal stability using a recently developed efficient annealing protocol, and investigated their atomic-scale dynamics systematically. We found that the dynamics of surface atoms remain invariant, whereas the difference in dynamical heterogeneity between surface and interior regions increases with the improvement of thermal stability. This can be associated with the more pronounced correlation between atomic activation energy spectra and depth from the surface in samples with higher thermal stability. In addition, dynamic anisotropy appears for surface atoms, and their transverse dynamics are faster than normal components, which can also be interpreted by activation energy spectra. Our results reveal the presence of strong liquid-like atomic dynamics confined to the surface of laboratory-made MGs, illuminating the underlying mechanisms for surface engineering design, such as cold joining by ultrasonic vibrations and superlattice growth.

TransCode:Uncovering COVID-19 transmission patterns via deep learning

Background The heterogeneity of COVID-19 spread dynamics is determined by complex spatiotemporal transmission patterns at a fine scale,especially in densely populated regions.In this study,we aim to discover such fine-scale transmission patterns via deep learning.Methods We introduce the notion of TransCode to characterize fine-scale spatiotemporal transmission patterns of COVID-19 caused by metapopulation mobility and contact behaviors.First,in Hong Kong,China,we construct the mobility trajectories of confirmed cases using their visiting records.Then we estimate the transmissibility of individual cases in different locations based on their temporal infectiousness distribution.Integrating the spatial and temporal information,we represent the TransCode via spatiotemporal transmission networks.Further,we propose a deep transfer learning model to adapt the TransCode of Hong Kong,China to achieve fine-scale transmission characterization and risk prediction in six densely populated metropolises:New York City,San Francisco,Toronto,London,Berlin,and Tokyo,where fine-scale data are limited.All the data used in this study are publicly available.Results The TransCode of Hong Kong,China derived from the spatial transmission information and temporal infectiousness distribution of individual cases reveals the transmission patterns(e.g.,the imported and exported transmission intensities)at the district and constituency levels during different COVID-19 outbreaks waves.By adapting the TransCode of Hong Kong,China to other data-limited densely populated metropolises,the proposed method outperforms other representative methods by more than 10%in terms of the prediction accuracy of the disease dynamics(i.e.,the trend of case numbers),and the fine-scale spatiotemporal transmission patterns in these metropolises could also be well captured due to some shared intrinsically common patterns of human mobility and contact behaviors at the metapopulation level.Conclusions The fine-scale transmission patterns due to the metapopulation level mobility(e.g.,travel across different districts)and contact behaviors(e.g.,gathering in social-economic centers)are one of the main contributors to the rapid spread of the virus.Characterization of the fine-scale transmission patterns using the TransCode will facilitate the development of tailor-made intervention strategies to effectively contain disease transmission in the targeted regions.