|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Bulk Metallic Glasses XIV
||Quasi-Elastic Neutron Scattering and Machine Learning Studies of the Arrhenius Crossover Phenomenon and Its Correlation with the Kinetic Fragility in Glass-Forming Metallic Liquids
||Abshishek Jaiswal, Yang Zhang
|On-Site Speaker (Planned)
Most metallic glasses are produced by quenching high-temperature metallic liquids sufficiently fast that the structural relaxation becomes essentially “frozen”. Therefore, an in-depth understanding of the relaxational dynamics of the metallic liquids and its connection to the kinetic fragility is important to unveil the atomic origin of the glass-forming abilities. We performed Quasi-Elastic Neutron Scattering (QENS) measurements of the mean effective diffusion coefficient of glass-forming metallic liquids in the generalized hydrodynamic regime and used machine learning algorithms to analyze the simulated atomic trajectory. We observed a universal Arrhenius crossover from high-temperature Arrhenius to low-temperature super-Arrhenius behavior at reduced Arrhenius crossover temperature θ<SUB>A</SUB> = T<SUB>A</SUB>/T<SUB>g</SUB>. By comparing with many other molecular and network liquids, we found a distinct correlation between the reduced Arrhenius crossover temperature θ<SUB>A</SUB> and the kinetic fragility index m. These observations provide a way to estimate the low-temperature glassy characteristics (T<SUB>g</SUB> and m) from high-temperature liquid quantities (E<SUB>∞</SUB>and θ<SUB>A</SUB>).
||Planned: Supplemental Proceedings volume