@article{oai:kyutech.repo.nii.ac.jp:00007568,
 author = {Mito, Masaki and 美藤, 正樹 and Mokutani, Narimichi and Tsuji, Hiroki and Tang, Yongpeng and Matsumoto, Kaname and 松本, 要 and Murayama, Mitsuhiro and Horita, Zenji and 堀田, 善治},
 issue = {10},
 journal = {Journal of Applied Physics},
 month = {Mar},
 note = {Aluminum (Al) and titanium (Ti) are superconducting materials but their superconducting transition temperatures (Tc) are quite low as 1.20 and 0.39 K, respectively, while magnesium (Mg) never exhibits superconductivity. In this study, we explored new superconductors with higher Tc in the Al–Mg–Ti ternary system, along with the prediction using machine learning. High-pressure torsion (HPT) is utilized to produce the superconducting states. While performing AC magnetization measurements, we found, for the first time, superconducting states with Tc=4.0 and 7.3 K for a composition of Al:Ti = 1:2. The magnetic anomalies appeared more sharply when the sample was processed by HPT at 573 K than at room temperature, and the anomalies exhibited DC magnetic field dependence characteristic of superconductivity. Magnetic anomalies also appeared at ∼55 and ∼93 K, being supported by the prediction using the machine learning for the Al–Ti–O system, and this suggests that Al–Ti oxides play an important role in the advent of such anomalies but that the addition of Mg could be less effective.},
 pages = {105903-1--105903-8},
 title = {Achieving Superconductivity with Higher Tc in Lightweight Al-Ti-Mg Alloys: Prediction using Machine Learning and Synthesis via High-Pressure Torsion Process},
 volume = {131},
 year = {2022},
 yomi = {ミトウ, マサキ and マツモト, カナメ and ホリタ, ゼンジ}
}