[15p-K209-1~11] 11.1 Fundamental properties

2:45 PM - 3:00 PM JST(5:45 AM - 6:00 AM UTC)Presentation by Applicant for JSAP Young Scientists Presentation Award

[15p-K209-5] Prediction of phase formation in layered perovskite arsenic fluorides

〇(M1)Sonosuke Kono^1,2, Yoichi Higashi², Yuki Iwasa², Izumi Hase², Ryo Maezono³, Taichiro Nishio¹, Hiraku Ogino², Kenta Hongo³ (1.Tokyo Univ. of Sci., 2.AIST, 3.JAIST)

Keywords:

layered perovskite compounds,machine learning,materials informatics

In this study, we developed a machine learning model to predict phase formability in multilayered perovskite compounds. To predict the formation of new materials, we utilized SISSO (Sure Independence Screening and Sparsifying Operator) to identify key determinants for phase formation and classify experimental phase formation data. We also evaluated the generalization performance of the prediction model. Additionally, we evaluated the generalization performatnce by the model with a various metric. In this presentation, we will report the details of our study and predict the phase formation in novel layered perovskite arsenic fluoride compounds.