A contribution to the Al–Pd–Cr phase diagram

Kowalski, W.; Grushko, B.; Pavlyuchkov, D.; Surowiec, M.

doi:10.1016/j.jallcom.2010.02.033

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…This observation is consistent with the previous results, which point to a decrease of this value from 16 at.% of Ni or Co via 10 at.% of Fe and 5 at.% of Mn [12] to 3 at.% for Cr [13].…”

Section: Introductionsupporting

confidence: 94%

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

Zaikina

Khorujaya

Pavlyuchkov

et al. 2011

Journal of Alloys and Compounds

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“…This observation is consistent with the previous results, which point to a decrease of this value from 16 at.% of Ni or Co via 10 at.% of Fe and 5 at.% of Mn [12] to 3 at.% for Cr [13].…”

Section: Introductionsupporting

confidence: 94%

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

Zaikina

Khorujaya

Pavlyuchkov

et al. 2011

Journal of Alloys and Compounds

Self Cite

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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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“…[14], the O-phase was not found in Ref. [34]). While the composition of C 3,I -Al-Cr-Fe was not determined in Ref.…”

Section: The Ternary Orthorhombic O-phasementioning

confidence: 89%