Comprehensive experimental datasets of quasicrystals and their approximants

Quasicrystals are solid-state materials that typically exhibit unique symmetries, such as icosahedral or decagonal diffraction symmetry. They were first discovered in 1984. Over the past four decades of quasicrystal research, around 100 stable quasicrystals have been discovered. In recent years, machine learning has been employed to explore quasicrystals with unique properties inherent to quasiperiodic systems. However, the lack of open data on quasicrystal composition, structure, and physical properties has hindered the widespread use of machine learning in quasicrystal research. This study involves a comprehensive literature review and manual data extraction to develop open datasets consisting of composition, structure types, phase diagrams, and sample fabrication processes for a wide range of stable and metastable quasicrystals and approximant crystals, as well as the temperature-dependent thermal, electrical, and magnetic properties.

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Publication Details

Journal
Scientific Data
Published
2024-11-13
DOI
https://doi.org/10.1038/s41597-024-04043-z
Citations
7
Primary Topic
Quasicrystal Structures and Properties
Type
article
Field-Weighted Citation Impact
0.82

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Comprehensive experimental datasets of quasicrystals and their approximants

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7 citations
Scientific Data
Quasicrystal Structures and Properties
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Comprehensive experimental datasets of quasicrystals and their approximants

Asuka Ishikawa, Koichi Kitahara, Ryo Yoshida, Ryuji Tamura, Kaoru Kimura, Erina Fujita, Yukari Katsura, Tomoya MATO, Chang Liu
article en
7 citations

Abstract

Quasicrystals are solid-state materials that typically exhibit unique symmetries, such as icosahedral or decagonal diffraction symmetry. They were first discovered in 1984. Over the past four decades of quasicrystal research, around 100 stable quasicrystals have been discovered. In recent years, machine learning has been employed to explore quasicrystals with unique properties inherent to quasiperiodic systems. However, the lack of open data on quasicrystal composition, structure, and physical properties has hindered the widespread use of machine learning in quasicrystal research. This study involves a comprehensive literature review and manual data extraction to develop open datasets consisting of composition, structure types, phase diagrams, and sample fabrication processes for a wide range of stable and metastable quasicrystals and approximant crystals, as well as the temperature-dependent thermal, electrical, and magnetic properties.

Scientific DataVol. 11(1)
University of Tsukuba (JP), Tokyo University of Science (JP), The Graduate University for Advanced Studies, SOKENDAI (JP), National Institute for Materials Science (JP), RIKEN Center for Advanced Intelligence Project (JP), The Institute of Statistical Mathematics (JP), Research Organization of Information and Systems (JP), National Defense Academy of Japan (JP)
Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science, Core Research for Evolutional Science and Technology
Openalex Percentile: Top 25%
Quasicrystal Structures and Properties
0.82
DOIOpen Access PDFOpenAlex
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