[16p-D209-1~14] 11.1 Fundamental properties

[16p-D209-11] Tc prediction of REBCO thin films by X-ray diffraction data and machine learning

〇Kaname Matsumoto¹, Yutaka Yoshida², Tomoki Osada², Yusuke Ichino³, keiichi Horio¹, Tomoya Horide¹, Ataru Ichinose⁴ (1.Kyshu Inst. Technol., 2.Nagoya Univ., 3.Aichi Inst. Technol., 4.CRIEPI)

Keywords:superconductor, critical temperature, machine learning

The goal of this study is to analyze XRD patterns of REBCO superconducting thin films by machine learning and deep learning, and of particular interest is to obtain correlation information between XRD patterns and material properties, namely critical temperature (Tc) and critical current density (Jc). Here, we prepared a dataset of XRD and measured Tc values of 860 available REBCO thin film samples, and attempted to perform regression analysis and classification of Tc using machine learning and neural networks.