Distribution of atomic rearrangement vectors in a metallic glass

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 ( Eact) for rearrangement for a single atom and how those distributions vary across the atoms in the system. We have used molecular dynamics simulations of Cu50Zr50 metallic glass below Tg 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 Eact. However, atoms with lower Eact (or higher rearrangement rates) generally explored fewer possible rearrangement vectors, as the low Eact 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.

[1]  D. Morgan,et al.  Mechanisms of bulk and surface diffusion in metallic glasses determined from molecular dynamics simulations , 2021, 2108.09426.

[2]  D. Morgan,et al.  Factors correlating to enhanced surface diffusion in metallic glasses. , 2021, The Journal of chemical physics.

[3]  R. Ritchie,et al.  Universal nature of the saddle states of structural excitations in metallic glasses , 2021, Materials Today Physics.

[4]  E. Ma,et al.  Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning , 2020, Nature Communications.

[5]  A. Shapeev,et al.  Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning , 2020, npj Computational Materials.

[6]  Pushmeet Kohli,et al.  Unveiling the predictive power of static structure in glassy systems , 2020 .

[7]  Weihua Wang,et al.  Fast surface dynamics enabled cold joining of metallic glasses , 2019, Science Advances.

[8]  M. Ediger Perspective: Highly stable vapor-deposited glasses. , 2017, The Journal of chemical physics.

[9]  R. Richert,et al.  Structural rearrangements governing Johari-Goldstein relaxations in metallic glasses , 2017, Science Advances.

[10]  Takeshi Egami,et al.  Energy landscape-driven non-equilibrium evolution of inherent structure in disordered material , 2017, Nature Communications.

[11]  Andrea J. Liu,et al.  Relationship between local structure and relaxation in out-of-equilibrium glassy systems , 2016, Proceedings of the National Academy of Sciences.

[12]  Andrea J. Liu,et al.  Structural Properties of Defects in Glassy Liquids. , 2016, The journal of physical chemistry. B.

[13]  Y. Yang,et al.  Unusual fast secondary relaxation in metallic glass , 2015, Nature Communications.

[14]  Andrea J. Liu,et al.  A structural approach to relaxation in glassy liquids , 2015, Nature Physics.

[15]  Jun Ding,et al.  Soft spots and their structural signature in a metallic glass , 2014, Proceedings of the National Academy of Sciences.

[16]  Stasa Milojevic,et al.  Power law distributions in information science: Making the case for logarithmic binning , 2010, J. Assoc. Inf. Sci. Technol..

[17]  M. Kramer,et al.  Development of suitable interatomic potentials for simulation of liquid and amorphous Cu–Zr alloys , 2009 .

[18]  David A. Weitz,et al.  Structural Rearrangements That Govern Flow in Colloidal Glasses , 2007, Science.

[19]  Robert J. McMahon,et al.  Organic Glasses with Exceptional Thermodynamic and Kinetic Stability , 2007, Science.

[20]  P. Harrowell,et al.  Free volume cannot explain the spatial heterogeneity of Debye–Waller factors in a glass-forming binary alloy , 2005, cond-mat/0511690.

[21]  M. Newman Power laws, Pareto distributions and Zipf's law , 2005 .

[22]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .