Formation of Complex Intermetallics in the Al-Rich Part of Al-Pt-Ru

Kapush, D.; Samuha, Shmuel; Meshi, Louisa; Velikanova, Т. Ya.; Grushko, B.

doi:10.1007/s11669-015-0385-3

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Unlike conventional materials, the yield strength of Ni 3 Al and its alloys increases with increasing temperature instead of falling. Ru-Al compounds have attracted great attention as a promising material for high-temperature application due to physical, chemical, and mechanical properties [7][8][9][10][11]. The B2phase RuAl compound, one of the Ru-Al compounds, has been extensively studied in recent years [12,13].…”

Section: Introductionmentioning

confidence: 99%

First-principles study on B2 based XAl(X = Rh, Ru)compounds

Durukan¹,

Çiftçi²

2021

Phys. Scr.

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In this study, to see pressure effects on optical, thermodynamic, structural, elastic, electronic properties, charge density, and phonon frequencies of the XAl (X:Rh, Ru) compounds in B2 structure, the first-principles methods were used. The ground-state properties of these compounds were determined and compared with experimental and theoretical data. High Young’s and shear modulus showed these compounds to be hard materials. The investigated compounds have ductile property according to the Paugh criterion and Poisson’s ratio calculated from elastic constants. The electronic band structure showed that these compounds have a metallic nature. Dynamic stability using phonon distribution curves was determined under pressure. The bond properties between Rh-Al and Ru-Al atoms were evaluated in detail by Mulliken Atomic Populations and charge density analysis. Also, the optical properties are examined in detail. We think that this theoretical work contributes greatly to engineering applications due to the electronic, thermodynamic, and optical behavior of XAl (X: Rh, Ru) compounds.

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Section: Introductionmentioning

confidence: 99%

First-principles study on B2 based XAl(X = Rh, Ru)compounds

Durukan¹,

Çiftçi²

2021

Phys. Scr.

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“…We verified the validity of the predicted phase diagrams based on the experimental stable quasicrystal and approximant phase regions of the 30 systems that were extracted from the literature. [ 34–58 ] We found 198 papers published by Prof. Grushko's group, which include ternary phase diagrams of Al–transition elements encompassing 64 unique alloy systems. Excluding the systems containing the 80 stable quasicrystal and 78 approximant compositions used for training, the remaining 30 systems were used for performance evaluation.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning to Predict Quasicrystals from Chemical Compositions

et al. 2021

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Quasicrystals have emerged as the third class of solid‐state materials, distinguished from periodic crystals and amorphous solids, which have long‐range order without periodicity exhibiting rotational symmetries that are disallowed for periodic crystals in most cases. To date, more than one hundred stable quasicrystals have been reported, leading to the discovery of many new and exciting phenomena. However, the pace of the discovery of new quasicrystals has lowered in recent years, largely owing to the lack of clear guiding principles for the synthesis of new quasicrystals. Here, it is shown that the discovery of new quasicrystals can be accelerated with a simple machine‐learning workflow. With a list of the chemical compositions of known stable quasicrystals, approximant crystals, and ordinary crystals, a prediction model is trained to solve the three‐class classification task and its predictability compared to the observed phase diagrams of ternary aluminum systems is evaluated. The validation experiments strongly support the superior predictive power of machine learning, with the overall prediction accuracy of the phase prediction task reaching ≈0.728. Furthermore, analyzing the input–output relationships black‐boxed into the model, nontrivial empirical equations interpretable by humans that describe conditions necessary for stable quasicrystal formation are identified.

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Characterization of Atomic Structures of Nanosized Intermetallic Compounds Using Electron Diffraction Methods

Meshi

Samuha²

2018

Advanced Materials

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In metallurgy, many intermetallic compounds crystallize as nanosized particles in metallic matrices. These particles influence dramatically the physical properties of engineering materials such as alloys and steels. Since properties and crystal structure are intimately linked, characterization of the atomic model of these intermetallides is crucial for the development of new alloys. However, this structural information usually cannot be attained using traditional X-ray diffraction methods, limited by the small volume and size of the precipitates. In these cases, electron diffraction (ED) is the most suitable method. In the last few decades, ED has experienced a tremendous leap forward. Many structures, including intermetallides, are solved using these methods. The class of intermetallides should be discussed independently since these phases do not comprise regular polyhedrals; moreover, the interatomic distances and angles vary drastically even in the same compositional system. These facts point to difficulties that have to be overcome during the solution path. Furthermore, intermetallic compounds can be of high complexity-possessing hundreds of atoms in the unit cell. Here, this topic is expanded with an emphasis on novel developments in the field.

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Formation of Complex Intermetallics in the Al-Rich Part of Al-Pt-Ru

Cited by 7 publications

References 19 publications

First-principles study on B2 based XAl(X = Rh, Ru)compounds

First-principles study on B2 based XAl(X = Rh, Ru)compounds

Machine Learning to Predict Quasicrystals from Chemical Compositions

Characterization of Atomic Structures of Nanosized Intermetallic Compounds Using Electron Diffraction Methods

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