2020
DOI: 10.1021/acs.jpclett.0c00495
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Soft-Mode Parameter as an Indicator for the Activation Energy Spectra in Metallic Glass

Abstract: The activation energy (EA) spectra of potential energy landscape (PEL) provides a convenient perspective for interpreting complex phenomena in amorphous materials; however, the link between the EA spectra and other physical properties in metallic glasses is still mysterious. By systematically probing the EA spectra for numerous metallic glass samples with distinct local geometric ordering, which correspond to broad processing histories, it is found that the shear modulus of the samples are strongly correlated … Show more

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Cited by 9 publications
(4 citation statements)
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“…energetic characteristics of GBs rather than their specific morphologies exert a greater influence on the strength and mobility [51]. Notably, such significant role of inherent energy in nanocrystalline systems have been identified in amorphous materials as well [30,[52][53][54].…”
Section: Gbs' Metastability-modulated Properties Changesmentioning
confidence: 99%
“…energetic characteristics of GBs rather than their specific morphologies exert a greater influence on the strength and mobility [51]. Notably, such significant role of inherent energy in nanocrystalline systems have been identified in amorphous materials as well [30,[52][53][54].…”
Section: Gbs' Metastability-modulated Properties Changesmentioning
confidence: 99%
“…S4). Furthermore, we analyze the local configurational anisotropy, |u i |, defining the deviation from the center of atom i to the centroid of its corresponding Voronoi cell [32][33][34]. For atoms in full icosahedra, the center of the atom and the corresponding Voronoi cell centroid coincide and |u i |=0.…”
Section: Science China Materialsmentioning
confidence: 99%
“…However, for atoms in distorted polyhedra, the atom's center deviates from the centroid of corresponding Voronoi polyhedron. Therefore, the local configurational anisotropy signifies the degree of symmetry breaking of atomic environment and thus the propensity for vibrations [32][33][34][35][36]. As shown in Fig.…”
Section: Science China Materialsmentioning
confidence: 99%
“…MGs’ excess low-frequency vibrational modes have spatial quasilocal characteristics and contribute to BP . The local regions with more participation in low-frequency vibrational modes are called “soft spots”, which are closely related to plastic events. These parameters have a certain predictive power on the performance of MGs but lack structural information. In addition, with the rapid development of computers, machine learning (ML) has also achieved unprecedented accuracy in predicting the local structural characteristics and other dynamics of MGs. , However, without physical relevance, the transferability and generalizability of ML models remain an issue.…”
mentioning
confidence: 99%