Quasicrystals are materials that do not have the translational symmetry of ordinary crystals, but have a high degree of order in their atomic arrangement. Since the first quasicrystal was discovered in 1984, approximately 100 thermally stable quasicrystals have been discovered. The discovery of new quasicrystals has led to the discovery of physical properties unique to quasiperiodic structures and new developments in materials science to unravel their mysteries. However, the mechanisms of quasicrystal formation and stabilization are still largely unknown, making the search for new quasicrystals extremely difficult.
 A research group of the Institute of Statistical Mathematics, Tokyo University of Science, and the University of Tokyo has successfully developed a machine learning algorithm to predict the chemical compositions that form thermally stable quasicrystals by learning the patterns of quasicrystals and related materials that have been synthesized to date. Based on the machine learning predictions, the group discovered three new quasicrystals (Al₆₅Ni₂₀Os₁₅, Al₇₈Ir₁₇Mn₅, and Al₇₈Ir₁₇Fe₅). These are the first quasicrystals discovered by machine learning algorithms in the 40-year history of quasicrystal research.
 The paper was published online in Physical Review Materials on September 25, 2023.

The Institute of Statistical Mathematics Press Release　(Sep 28, 2023)
https://www.ism.ac.jp/ura/press/ISM2023-05_e.html

Paper published
Title: Quasicrystals predicted and discovered by machine learning
Authors: Chang Liu, Koichi Kitahara, Asuka Ishikawa, Takanobu Hiroto, Alok Singh, Erina Fujita, Yukari Katsura, Yuki Inada, Ryuji Tamura, Kaoru Kimura, Ryo Yoshida
Journal: Physical Review Materials
DOI: 10.1103/PhysRevMaterials.7.093805
Publication date: September 25, 2023 (electronic version)