Mechanical performance dataset for alloy with applications at low temperatures
Abstract Modern technologies such as liquid fuels (hydrogen, oxygen), superconductivity, and quantum technology require materials to serve at very low temperatures, pushing the bounds of material performance by demanding a combination of strength and toughness to tackle various challenges. Steel all...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05512-9 |
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| Summary: | Abstract Modern technologies such as liquid fuels (hydrogen, oxygen), superconductivity, and quantum technology require materials to serve at very low temperatures, pushing the bounds of material performance by demanding a combination of strength and toughness to tackle various challenges. Steel alloys are among the most commonly used materials in cryogenic applications. Meanwhile, aluminum and titanium alloys are increasingly recognized for their potential in aerospace and the transportation sectors. Emerging multi-principal element alloys such as medium-entropy and high-entropy alloys offer superior low-temperature mechanical performance, greatly expanding the space for material design. A comprehensive dataset of these low-temperature metallic alloys has been curated from literature and made available in an open repository to meet the need for validated data sources in research and development. The dataset construction workflow incorporates automated extraction using state-of-the-art machine learning and natural language processing techniques, supplemented by manual inspection and correction to improve data extraction efficiency and ensure dataset quality. The product dataset encompasses key performance parameters such as yield strength, tensile strength, elongation at fracture, and Charpy impact energy. The accompanying metadata, detailing material types, chemical compositions, processing and testing conditions, are provided in a standardized format to promote data-driven research in material screening, design, and discovery. |
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| ISSN: | 2052-4463 |