journal article·2025-04

Uncovering the microstructural origins of shear transformation events in metallic glasses: Insights from combining knowledge and data

Tao Long, Zhilin Long, Bo Pang

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OpenAlex topics: Metallic Glasses and Amorphous Alloys · Cultural Heritage Materials Analysis

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References (82)

Show source-provided references
1. Dislocation nucleation governed softening and maximum strength in nano-twinned metals10.1038/nature08929 ↗
2. Universality class of nanocrystal plasticity: Localization and self-organization in discrete dislocation dynamics10.1103/PhysRevLett.122.178001 ↗
3. Deformation of metallic glasses: Recent developments in theory, simulations, and experiments10.1016/j.actamat.2016.01.049 ↗
4. Toward atomic-scale understanding of structure-dynamics-properties relations for metallic glasses10.1016/j.pmatsci.2024.101311 ↗
5. Plastic deformation in metallic glasses10.1016/0001-6160(79)90055-5 ↗
6. Complexity of plastic instability in amorphous solids: Insights from spatiotemporal evolution of vibrational modes10.1140/epje/i2020-11983-6 ↗
7. The dynamics of shear band propagation in metallic glasses10.1016/j.actamat.2023.118787 ↗
8. Anomalous temperature dependence of elastic limit in metallic glasses10.1038/s41467-023-44048-7 ↗
9. Connecting local yield stresses with plastic activity in amorphous solids10.1103/PhysRevLett.117.045501 ↗
10. Predicting shear transformation events in metallic glasses10.1103/PhysRevLett.120.125503 ↗
11. Structural signature of plastic deformation in metallic glasses10.1103/PhysRevLett.106.135503 ↗
12. Predicting plasticity in disordered solids from structural indicators
13. Mechanics of amorphous solids (in Chinese)10.1360/TB-2022-0181 ↗
14. Atomic level stresses10.1016/j.pmatsci.2011.01.004 ↗
15. Random packings and the structure of simple liquids. I. The geometry of random close packing
16. Five-fold symmetry as indicator of dynamic arrest in metallic glass-forming liquids10.1038/ncomms9310 ↗
17. Correlation between local atomic symmetry and mechanical properties in metallic glasses10.1016/j.jmst.2014.05.001 ↗
18. Soft mode parameter as an indicator for the activation energy spectra in metallic glass10.1021/acs.jpclett.0c00495 ↗
19. Universal structural parameter to quantitatively predict metallic glass properties10.1038/ncomms13733 ↗
20. Depicting defects in metallic glasses by atomic vibrational entropy10.1021/acs.jpclett.3c01674 ↗
21. Identifying structural flow defects in disordered solids using machine-learning methods10.1103/PhysRevLett.114.108001 ↗
22. Machine-learning integrated glassy defect from an intricate configurational-thermodynamic-dynamic space10.1103/PhysRevB.104.064108 ↗
23. Identification of atomic rearrangements in amorphous alloys based on machine learning10.1016/j.jmrt.2023.11.234 ↗
24. Machine learning bridges local static structure with multiple properties in metallic glasses10.1016/j.mattod.2020.05.021 ↗
25. Machine-learning inspired density-fluctuation model of local structural instability in metallic glasses10.1016/j.actamat.2023.118741 ↗
26. Quantitative structure-plasticity relationship in metallic glass: A machine learning study10.1088/1674-1056/abdda5 ↗
27. Identifying packing features of atoms with distinct dynamic behaviors in metallic glass by machine-learning method10.1007/s40843-020-1626-3 ↗
28. Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning10.1038/s41524-020-00467-4 ↗
29. A transferable machine-learning framework linking interstice distribution and plastic heterogeneity in metallic glasses10.1038/s41467-019-13511-9 ↗
30. Predicting orientation-dependent plastic susceptibility from static structure in amorphous solids via deep learning10.1038/s41467-021-21806-z ↗
31. Evaluating the predictive power of machine learning model for shear transformation in metallic glasses using metrics for an imbalanced dataset10.3389/fmats.2022.874339 ↗
32. Inverse design of glass structure with deep graph neural networks10.1038/s41467-021-25490-x ↗
33. Toward interpreting the thermally activated β dynamics in metallic glass using the structural constraint neural network.10.1021/acs.jpclett.4c00280 ↗
34. Fast parallel algorithms for short-range molecular dynamics10.1006/jcph.1995.1039 ↗
35. Visualization and analysis of atomistic simulation data with OVITO-the open visualization tool
36. Atomic level structure in multicomponent bulk metallic glass10.1103/PhysRevLett.102.245501 ↗
37. Experimental and molecular dynamics simulation study of structure of liquid and amorphous Ni62Nb38 alloy10.1063/1.4968212 ↗
38. Development of interatomic potentials appropriate for simulation of devitrification of Al90Sm10 alloy10.1088/0965-0393/23/4/045013 ↗
39. Relating dynamic properties to atomic structure in metallic glasses10.1007/s11837-012-0360-y ↗
40. An embedded-atom potential for the Cu-Ag system10.1088/0965-0393/14/5/002 ↗
41. Short-range structural signature of excess specific heat and fragility of metallic-glass-forming supercooled liquids10.1103/PhysRevB.85.060201 ↗
42. A unified formulation of the constant temperature molecular dynamics methods10.1063/1.447334 ↗
43. Canonical dynamics: Equilibrium phase-space distributions10.1103/PhysRevA.31.1695 ↗
44. Local structural fluctuations in amorphous and liquid metals: a simple theory of the glass transition10.1088/0305-4608/12/10/010 ↗
45. Atomic-level structure and structure–property relationship in metallic glasses10.1016/j.pmatsci.2010.12.002 ↗
46. A structural approach to relaxation in glassy liquids10.1038/nphys3644 ↗
47. Quantifying disorder one atom at a time using an interpretable graph neural network paradigm10.1038/s41467-023-39755-0 ↗
48. Klicpera · 2020
49. A microscopic mechanism for steady state inhomogeneous flow in metallic glasses10.1016/0001-6160(77)90232-2 ↗
50. Free volume cannot explain the spatial heterogeneity of Debye–Waller factors in a glass-forming binary alloy10.1016/j.jnoncrysol.2006.01.136 ↗
51. Predicting the long-time dynamic heterogeneity in a supercooled liquid on the basis of short-time heterogeneities10.1103/PhysRevLett.96.185701 ↗
52. Structural rearrangements that govern flow in colloidal glasses10.1126/science.1149308 ↗
53. Free volume evolution in metallic glasses subjected to mechanical deformation10.2320/matertrans.MJ200785 ↗
54. Amorphous systems in athermal, quasistatic shear10.1103/PhysRevE.74.016118 ↗
55. Dynamics of viscoplastic deformation in amorphous solids10.1103/PhysRevE.57.7192 ↗
56. Kyurkchiev · 2015
57. Elastic interactions of plastic events in strained amorphous solids before yield
58. Atomic-level structural identification for prediction of localized shear deformation in metallic glasses10.1016/j.ijsolstr.2020.01.012 ↗
59. Strain-dependent activation energy of shear transformation in metallic glasses10.1103/PhysRevB.95.144201 ↗
60. Potential energy landscape activations governing plastic flows in glass rheology10.1073/pnas.1907317116 ↗
61. Elastic avalanches reveal marginal behavior in amorphous solids10.1073/pnas.1915070117 ↗
62. Spatial heterogeneity as the structure feature for structure–property relationship of metallic glasses10.1038/s41467-018-06476-8 ↗
63. Local elastic properties of a metallic glass10.1038/nmat3024 ↗
64. A quasi-phase perspective on flow units of glass transition and plastic flow in metallic glasses10.1016/j.jnoncrysol.2013.04.053 ↗
65. Signature of local stress states in the deformation behavior of metallic glasses10.1038/s41427-020-00241-4 ↗
66. Structural feature of Cu64Zr36 metallic glass on nanoscale: Densely-packed clusters with loosely-packed surroundings10.1016/j.scriptamat.2015.12.038 ↗
67. Correlation between initial structure and athermal quasi-static compressive deformation in a metallic glass10.1016/j.jallcom.2016.12.366 ↗
68. Correlation of atomic cluster symmetry and glass-forming ability of metallic glass10.1103/PhysRevLett.99.095501 ↗
69. Entropy based fingerprint for local crystalline order10.1063/1.4998408 ↗
70. Atomic vibration as an indicator of the propensity for configurational rearrangements in metallic glasses10.1039/D1MH00491C ↗
71. Strain gradient drives shear banding in metallic glasses10.1103/PhysRevB.96.094103 ↗
72. Nanometer-scale gradient atomic packing structure surrounding soft spots in metallic glasses10.1038/s41524-018-0097-4 ↗
73. Flow units as dynamic defects in metallic glassy materials10.1093/nsr/nwy084 ↗
74. Second-nearest-neighbor correlations from connection of atomic packing motifs in metallic glasses and liquids10.1038/srep17429 ↗
75. Substantially enhanced plasticity of bulk metallic glasses by densifying local atomic packing10.1038/s41467-021-26858-9 ↗
76. Atomistic free-volume zones and inelastic deformation of metallic glasses.10.1038/nmat2802 ↗
77. The use of the area under the ROC curve in the evaluation of machine learning algorithms10.1016/S0031-3203(96)00142-2 ↗
78. Classification assessment methods
79. Dropout: a simple way to prevent neural networks from overfitting
80. Optuna: A next-generation hyperparameter optimization framework
81. Guiding experiment with machine learning: A case study of biochar adsorption of ciprofloxacin10.1016/j.seppur.2023.126023 ↗
82. A unified approach to interpreting model predictions