Machine Learning Accelerated Design of High-Temperature Ternary and Quaternary Nitride Superconductors

被引：1

作者：

Islam, Md Tohidul ^{[1
]}

Liu, Qinrui ^{[1
]}

Broderick, Scott ^{[1
]}

机构：

[1] SUNY Buffalo, Dept Mat Design & Innovat, Buffalo, NY 14260 USA

来源：

APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 20期

关键词：

superconductor; materials design; ternary nitrides; quaternary nitrides; elemental substitution; machine learning; high-temperature superconductors; THIN-FILMS; TITANIUM; NIOBIUM;

D O I：

10.3390/app14209196

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

The recent advancements in the field of superconductivity have been significantly driven by the development of nitride superconductors, particularly niobium nitride (NbN). Multicomponent nitrides offer a promising platform for achieving high-temperature superconductivity. Beyond their high superconducting transition temperature (Tc), niobium-based compounds are notable for their superior superconducting and mechanical properties, making them suitable for a wide range of device applications. In this work, machine learning is used to identify ternary and quaternary nitrides, which can surpass the properties of binary NbN. Specifically, Nb0.35Ta0.23Ti0.42N shows an 84.95% improvement in Tc compared to base NbN, while the ternary composition Nb0.55Ti0.45N exhibits a 17.29% improvement. This research provides a valuable reference for the further exploration of high-temperature superconductors in diversified ternary and quaternary compositions.

引用

页数：14

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[33] Detection of Series Faults in High-Temperature Superconducting DC Power Cables Using Machine Learning [J].

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[34] Design considerations for SMES systems using MgB2 and/or high-temperature superconductors [J].

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[35] Machine learning accelerated design of a family of AlxCrFeNi medium entropy alloys with superior high temperature mechanical and oxidation properties [J].

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[36] High-current conductors using high-temperature superconductors [J].

Harrison, MR ;

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Jacobson, DM ;

Dineen, C .

GEC JOURNAL OF RESEARCH, 1996, 13 (03) :146-158

[37] Machine learning techniques for estimating high-temperature mechanical behavior of high strength steels [J].

Yazici, C. ;

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[38] PROPERTIES OF HIGH-TEMPERATURE SUPERCONDUCTORS (HTS) AND SYNTHESIS TECHNOLOGY [J].

Tolendiuly, S. ;

Alipbayev, K. ;

Fomenko, S. ;

Sovet, A. ;

Zhauyt, A. .

METALURGIJA, 2021, 60 (1-2) :137-140

[39] Calculated magnetic exchange interactions in high-temperature superconductors [J].

Wan, Xiangang ;

Maier, Thomas A. ;

Savrasov, Sergej Y. .

PHYSICAL REVIEW B, 2009, 79 (15)

[40] Emperical model of the microwave properties of high-temperature superconductors [J].

Vendik, OG ;

Vendik, IB ;

Kaparkov, DI .

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES, 1998, 46 (05) :469-478

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