Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
An approach to supervised classification and regression of superconductive materials is proposed which builds on the DeepSet technology. This enables us to provide the chemical constituents of the examined compounds as an input to the algorithm, while avoiding artefacts that could originate from the chosen ordering in the list. The performance of the method are successfully challenged for both classification (tag a given material as superconducting) and regression (quantifying the associated critical temperature). We then searched through the International Mineralogical Association list with the trained neural network. Among the obtained superconducting candidates, three materials were selected to undergo a thorough experimental characterization. Superconductivity has been indeed confirmed for the synthetic analogue of michenerite, PdBiTe, and observed for the first time in monchetundraite, Pd2NiTe2, at critical temperatures in good agreement with the theory predictions. This latter is the first certified superconducting material to be identified by artificial intelligence methodologies.
An approach to supervised classification and regression of superconductive materials is proposed which builds on the DeepSet technology. This enables us to provide the chemical constituents of the examined compounds as an input to the algorithm, while avoiding artefacts that could originate from the chosen ordering in the list. The performance of the method are successfully challenged for both classification (tag a given material as superconducting) and regression (quantifying the associated critical temperature). We then searched through the International Mineralogical Association list with the trained neural network. Among the obtained superconducting candidates, three materials were selected to undergo a thorough experimental characterization. Superconductivity has been indeed confirmed for the synthetic analogue of michenerite, PdBiTe, and observed for the first time in monchetundraite, Pd2NiTe2, at critical temperatures in good agreement with the theory predictions. This latter is the first certified superconducting material to be identified by artificial intelligence methodologies.
Palladothallite, Pd3Tl, is a new mineral discovered in the Monchetundra layered intrusion, Kola Peninsula, Russia. Palladothallite occurs in orthopyroxenite with disseminated Ni-Cu-Fe sulfides and in near-surface oxidized ore of an orthopyroxenite unit. In the holotype specimen, the new mineral forms anhedral grains about 1 to 20 μm in size intergrown with bortnikovite (Pt4Cu3Zn). Palladothallite and bortnikovite form a rim around tulameenite (Pt2FeCu), Pt-Pd-Fe-Cu alloys, and Pt-Pd-Fe-Cu “oxides” in a goethite matrix. In plane-polarized light, palladothallite is white, anisotropy was not observed; it exhibits no internal reflections. Reflectance values of palladothallite in air (R' in %) are: 53.9 at 470 nm, 57.1 at 546 nm, 59.4 at 589 nm and 61.7 at 650 nm. Twelve electron probe microanalyses of palladothallite gave an average composition (in wt.%): Pd 59.99, Cu 1.19, Fe 0.35, Ag 1.1, Tl 35.64, Se 0.34, and S 0.09, total 99.67, corresponding to the empirical formula (Pd2.894Cu0.096Fe0.032Ag0.053)∑3.075(Tl0.895Se0.023S0.008)∑0.926 based on four atoms, with the ideal formula Pd3Tl. The density, calculated on the basis of the empirical formula, is 13.04 g/cm3. Palladothallite crystallizes with the same structure as synthetic Pd3Tl, which was solved by Kurtzemann & Kohlmann (2010) from powder neutron diffraction data. Palladothallite is tetragonal, space group I4/mmm, with a 4.10659(9), c 15.3028(4) Å, V 258.07(1) Å3, and Z = 4. Palladothallite crystallizes in the ZrAl3 structure type. The name corresponds to its chemical composition, palladium and thallium.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.