Machine Learning to Predict Quasicrystals from Chemical Compositions (Adv. Mater. 36/2021). Issue 36 (9th September 2021)

• Record Type: Journal Article
• Authors: Liu, Chang; Fujita, Erina; Katsura, Yukari; Inada, Yuki; Ishikawa, Asuka; Tamura, Ryuji; Kimura, Kaoru; Yoshida, Ryo
• Is Part Of: Advanced materials. Volume 33:Issue 36(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-09
• Subjects: approximant crystals -- high‐throughput screening -- machine learning -- materials informatics -- quasicrystals; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202170284
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract : Quasicrystals In article number 2102507, Kaoru Kimura, Ryo Yoshida, and co‐workers demonstrate that machine‐learning algorithms can predict the chemical composition of new quasicrystals. Furthermore, analyzing the input–output relationships black‐boxed into the machine‐learning model, they successfully identify nontrivial empirical equations interpretable by humans that describe the conditions necessary for stable quasicrystal formation. This is the first step toward understanding the formation mechanism of quasicrystals, which has been long sought in quasicrystal research.