Journal Article

Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns

Kimura, Kaoru, Tamura, Ryuji, Uryu, Hirotaka, Miyao, Naoya, Hiroki, Kanta, Yamada, Tsunetomo, Singh, Alok, Ohhashi, Satoshi, Ishikawa, Asuka, Yoshida, Ryo, Iwasaki, Yutaka, Kitahara, Koichi, Liu, Chang

2024

Overview

Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X‐ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase‐identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al–Si–Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well‐trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al–Si–Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample.

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Accuracy / Alloys / Datasets / Deep learning / deep neural networks / icosahedral quasicrystals / Identification / machine learning / phase‐identification / powder X‐ray diffraction