Investigation of the Al–Ti–Pd alloy system at 930 and 1100°C

Zaikina, O. V.; Khorujaya, V.G.; Pavlyuchkov, D.; Grushko, B.; Velikanova, Т. Ya.

doi:10.1016/j.jallcom.2010.08.144

Cited by 8 publications

(9 citation statements)

References 13 publications

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“…That is, the alloy falls into the τ 1 + τ 3 + β 0 three-phase field, whose existence is also confirmed by EMPA and XRD data for other three-phase alloys in this region (Table 2). Our further analysis of the system at 1100°C revealed that the τ 2 phase formed in a solid state between 1050 and 1100°C [11] (solidus temperature of the alloy whose composition corresponds to the stoichiometry of this phase is 1150°C) ( Table 2). As is already noted, the τ 1 and τ 3 ternary compounds coexist in equilibrium with the β 0 phase at subsolidus temperatures (Fig.…”

Section: Resultsmentioning

confidence: 93%

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Solidus surface of the Al–Ti–Pd system in the Al–AlPd–TiPd–Ti region

Zaikina

Khoruzha

Korniyenko

et al. 2011

Powder Metall Met Ceram

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The Al-Ti-Pd system has been studied by microstructural analysis, electron microprobe analysis, xray diffraction, and differential temperature analysis in the composition range 0-50 at.% Pd (AlAlPd-TiPd-Ti partial system) at subsolidus temperatures. The solidus surface of the system is constructed for the first time. At subsolidus temperatures, there are two ternary compounds, τ 1 (Al 61 Ti 26 Pd 13 , AuCu 3 type) and τ 3 (Al 44 Ti 35 Pd 21 , MgZn 2 type), a continuous solid solution (β 0 phase) between isostructural (CsCl type) high-temperature modifications of AlPd and TiPd equiatomic phases, and terminal solid solutions based on β-Ti and α-Ti and TiAl, Ti 2 Al 5 , TiAl 3 , and Al 3 Pd 2 binary compounds and ε phase. The above-mentioned phases participate in 11 four-phase invariant equilibria involving the liquid phase and are represented by isothermal tie-line triangles in the temperature range between 630 and 1205°C.

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Section: Resultsmentioning

confidence: 93%

“…Since there are no alloys in the relevant regions, the nature of phase equilibria and positions of the tie-line triangles were determined by analogy with those established at 1100°C [11]: τ 1 + <TiAl> + <Ti 2 Al 5 > and τ 1 + <Ti 2 Al 5 > + <TiAl 3 >.…”

Section: Resultsmentioning

confidence: 99%

Solidus surface of the Al–Ti–Pd system in the Al–AlPd–TiPd–Ti region

Zaikina

Khoruzha

Korniyenko

et al. 2011

Powder Metall Met Ceram

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“…As mentioned in Ref. [5] and also applicable here, both diagrams in Refs. [12,13] are consistent in the temperature range relevant to our study.…”

Section: Introductionmentioning

confidence: 76%

“…Five ternary phases designated 1 to 5 were reported in Refs. [1][2][3][4][5] (see Table 1 for structural details).…”

Section: Introductionmentioning

confidence: 99%

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Investigation of the Al–Ti–Pt alloy system at 1100°C

Zaikina

Khorujaya

Pavlyuchkov

et al. 2011

Journal of Alloys and Compounds

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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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Investigation of the Al–Ti–Pd alloy system at 930 and 1100°C

Cited by 8 publications

References 13 publications

Solidus surface of the Al–Ti–Pd system in the Al–AlPd–TiPd–Ti region

Solidus surface of the Al–Ti–Pd system in the Al–AlPd–TiPd–Ti region

Investigation of the Al–Ti–Pt alloy system at 1100°C

Machine Learning to Predict Quasicrystals from Chemical Compositions

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