Owing to limitations in the spatial and temporal resolution of the current experimental research technologies, the heterogeneity of a disordered structure poses a great challenge to the experimental study of atomic-level behaviors of amorphous alloys. Computational simulation can be a powerful tool in the understanding of such amorphous structures and their response at the atomic level. However, owing to the limitations of multielement interactions, computational approaches, and computational capability, there is still an insurmountable gap between the model systems used in computational simulation and real amorphous alloy materials. Combining the power of the modern computing technology, software, and algorithms, the exploration and development of hihgly effective computational approaches that can be applied to the simulation of amorphous alloys is a potential way to address this long-term challenge. This article reviews recent progress in the computational study of atomic structure and structural instability in metallic glasses, the role that such computational approaches can play in the understanding and the modification of material properties, and in the optimization of material preparation. A brief perspective on the research areas of the computational simulation of metallic glasses is also proposed.
Fig.1 The hybrid packing method and local structural distortion in PdNiP metallic glasses. Typical atomic configuration of glassy Pd40Ni40P20. The connection between P-centered TTPs and Ni-centered icosahedra is highlighted, illustrating a topological order between the two clusters. FS, ES, and VS denote the face, edge, and vertex sharing methods between P-centered clusters. The dashed circles delineate the Ni-centered icosahedron-like polyhedra (TTP—tricapped trigonal prism)[30]
Fig.2 Softness of quasi-localized modes and the probability distribution of vibrational amplitude () in poorly annealed glasses and the ultrastable glasses, respectively[41]
Fig.3 The cooling rate and pressure dependence of the structure and dynamics response heterogeneity of metallic glasses. Reduced loss modulus (E″) as a function of scaled temperature () (a)[54] and the fraction of full icosahedra () (b) (T—temperature, Tα—the temperature at which the E″ curve exhibits a peak corresponding to the α-relaxation, which signals the transition from glassy to supercooled liquid states; S1-S3, P1, and P3 denoted samples with different thermal histories)
Fig.4 Decoupling between the local clusters and the structural relaxation in confined metallic glass forming liquids[55]
Fig.5 The mechanical heterogeneity of metallic glasses in the elastic regime and the dimensionality and characteristic correlation length of most active atoms, which can be treated as flow units (STZ—shear transformation zone; arrows mark the representative flow units)
Fig.6 The universal link between and the non-Gaussian parameter of Cu50Zr50 metallic glasses under different quenched conditions (tp—the time interval of the dynamic mechanical spectroscopy simulations, subscript “peak” indicates the value of α2 or at the peak positions in Fig.6a)[64]
Fig.7 The universal link between the reduced structural relaxation time and the maximum of non-Gaussian parameter ()for various glass-forming liquids ( is the characteristic time scale under constant dynamic heterogeneity condition)[65,66]
Fig.8 The analogous evolution of the correlation lengthscale of activated flow units under the unfreezing process (a)[60] and the applied strain (b)[61]
Fig.9 Two-dimensional plot of viscosity as a function of temperature and stress in a model metallic glass (T0 = 860 K, σ0—the critical stress where η0 diverges in extrapolation to T = 0?K, σ—stress, η—viscosity, η0—the viscosity to define the glass transition)[69]
Fig.10 The variation of the structural instability pattern in metallic glasses originates from the competence between the activation of flow units and the cavitation nucleation under different applied conditions [71]
Fig.11 The stress-induced glass-to-liquid transition and the dynamical response heterogeneity during the cold joining of metallic glasses[72]
Fig.12 The history and basic characteristics of metallic glass structural models
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