Impact of hybridization on metallic-glass formation and design

Yuan, Changxia; Yang, Fan; Xi, Xuekui; Shi, Chenglong; Holland‐Moritz, D.; Li, M.Z; Hu, Feng; Shen, Baolong; Wang, X.L; Meyer, Andreas; Wang, W.H

doi:10.1016/j.mattod.2019.06.001

Cited by 40 publications

(14 citation statements)

References 63 publications

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“…Here, it has to be pointed that the small number of Al-Al interactions shown in Table I is intrinsic to the material. It follows upon microalloying of Al and it has already been described in the literature as a solute-solute avoidance effect [24,25]. As discussed in the Supporting Information, concerning the abovementioned transferability, it was not a complicating factor when further applying the ML model, and a single Al-Al DBI was set up by merging the three related DBIs listed in Table I.…”

Section: A the Machine Learning-based Approachmentioning

confidence: 99%

Chemical Bonding in Metallic Glasses from Machine Learning and Crystal Orbital Hamilton Population

Ferreira

2020

Preprint

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The chemistry (composition and bonding information) of metallic glasses (MGs) is at least as important as structural topology for understanding their properties and production/processing peculiarities. This article reports a machine learning (ML)-based approach that brings an unprecedented "big picture" view of chemical bond strengths in MGs of a prototypical alloy system. The connection between electronic structure and chemical bonding is given by crystal orbital Hamilton population (COHP) analysis within the framework of density functional theory (DFT). The stated comprehensive overview is made possible through a combination of efficient quantitative estimate of bond strengths supplied by COHP analysis, representative statistics regarding structure in terms of atomic configurations achieved with classical molecular dynamics simulations, and the smooth overlap of atomic positions (SOAP) descriptor. The study is supplemented by an application of that ML model under the scope of mechanical loading, in which predicted bond strengths enabled atom categorization based on a descriptor of the short-range order possessing a superior chemical sense; a key component to uncover structural/chemical heterogeneity and its influence on mechanical relaxation processes and atomic scale flow mechanisms in MGs.

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Section: A the Machine Learning-based Approachmentioning

confidence: 99%

Chemical Bonding in Metallic Glasses from Machine Learning and Crystal Orbital Hamilton Population

Ferreira

2020

Preprint

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“…In addition, both theoretical and experimental results demonstrated the significant impact of atomic bonding on the properties of amorphous alloys, which require the fundamental understanding of electronic structures. [ 15–17,53,54 ]…”

Section: Atomic Bonding Featuresmentioning

confidence: 99%

“…Recently, the formation mechanism of amorphous alloys is revealed at the electronic level using NMR and ab initio MD simulations. [ 54 ] The isotropic Knight shifts ( K iso ) and the spin–lattice relaxation times ( T 1 ) of amorphous Zr 72− x Co 28 Al x ribbons are shown as a function of Al concentration in NMR spectra ( Figure a). K iso initially decreases (region a) and then increases (region b) with Al concentration.…”

Section: Atomic Bonding Featuresmentioning

confidence: 99%

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Structures and Functional Properties of Amorphous Alloys

et al. 2020

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Amorphous alloys have attracted great attention due to their distinctive properties derived from unique packing structures. Recently, significant advances have been achieved for the understanding of structural characteristics and functional applications of amorphous alloys. Herein, an overview of the state of art of structure studies, accounting for the characteristics of amorphous alloys, are presented, and recent progresses in the functional applications of amorphous alloys are highlighted. The various structural models for the short‐range order, medium‐range order, and long‐range topological order for amorphous alloys are introduced. The functional applications in electrochemistry, mechanism, magnetism, optics, and biomedical engineering are presented in detail. The fundamental understanding of the correlations between structures and properties in amorphous alloys is discussed.

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“…13,15,21 For purely metallic alloys with comparable atomic radii, this approximation works quite well. Since Sulfur is a nonmetallic element, some amount of covalent bonds could be involved as previously observed for BMGs like Zr-(Ni,Co,Cu)-Al [22][23][24][25][26][27][28] and Pd-Ni-P. 29 In this case, specific chemical interactions may invalidate the assumption of tabulated atomic radii.…”

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confidence: 92%

Impact of Sulfur on the melt dynamics of glass forming Ti75Ni25−xSx

Wilden

Yang

Holland‐Moritz

et al. 2020

Applied Physics Letters

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Substitution of Nickel by Sulfur leads to bulk metallic glass formation in eutectic Ti 75 Ni 25 . In order to understand the improved glass forming ability, a detailed analysis of the melt dynamics was performed. We measured the density, the viscosity, and the self-diffusion of Ti 75 Ni 25 x S x (x ¼ 0, 5, 8) melts as a function of temperature. Moreover, the viscosity was measured under reduced gravity conditions to benchmark ground-based data. The self-diffusion coefficients were probed by incoherent, quasi-elastic neutron scattering. All measurements were combined with electrostatic or electromagnetic levitation, in order to process these highly reactive Titanium-based melts. From the measured density, the packing fraction was calculated as a function of temperature. In contrast to other bulk metallic glasses, the decrease in melt dynamics comes along with the decrease in packing density.

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Impact of hybridization on metallic-glass formation and design

Cited by 40 publications

References 63 publications

Chemical Bonding in Metallic Glasses from Machine Learning and Crystal Orbital Hamilton Population

Chemical Bonding in Metallic Glasses from Machine Learning and Crystal Orbital Hamilton Population

Structures and Functional Properties of Amorphous Alloys

Impact of Sulfur on the melt dynamics of glass forming Ti75Ni25−xSx

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