Fragility crossover mediated by covalent-like electronic interactions in metallic liquids  

作　　者：Hui-Ru Zhang Liang Gao Yu-Hao Ye Jia-Xin Zhang Tao Zhang Qing-Zhou Bu Qun Yang Zeng-Wei Zhu Shuai Wei Hai-Bin Yu 

机构地区：[1]Wuhan National High Magnetic Field Center and School of Physics,Huazhong University of Science and Technology,Wuhan 430074,People’s Republic of China [2]Department of Chemistry,Aarhus University,8000 Aarhus,Denmark [3]iMAT Centre for Integrated Materials Research,Aarhus University,8000 Aarhus,Denmark

出　　处：《Materials Futures》2024年第2期117-130,共14页材料展望（英文）

基　　金：National Thousand Young Talents Program of China,and the National Natural Science Foundation of China(NSFC 52201180).

摘　　要：Fragility is one of the central concepts in glass and liquid sciences,as it characterizes the extent of deviation of viscosity from Arrhenius behavior and is linked to a range of glass properties.However,the intervention of crystallization often prevents the assessment of fragility in poor glass-formers,such as supercooled metallic liquids.Hence experimental data on their compositional dependence are scarce,let alone fundamentally understood.In this work,we use fast scanning calorimetry to overcome this obstacle and systematically study the fragility in a ternary La–Ni–Al system,over previously inaccessible composition space.We observe fragility dropped in a small range with the Al alloying,indicating an alloying-induced fragility crossover.We use x-ray photoelectron spectroscopy,resistance measurements,electronic structure calculations,and DFT-based deep-learning atomic simulations to investigate the cause of this fragility drop.These results show that the fragility crossover can be fundamentally ascribed to the electronic covalency associated with the unique Al–Al interactions.Our findings provide insight into the origin of fragility in metallic liquids from an electronic structure perspective and pave a new way for the design of metallic glasses.

关 键 词：metallic glass FRAGILITY fast scanning calorimetry density functional theory deep learning potential 

分 类 号：TG14[一般工业技术—材料科学与工程]