[Mon-I1] Data-driven materials science and process informatics

[Mon-I1-3] Origin of Sintering Resistance of Pd/Sr₃Ti₂O₇ under the Three-way Catalyst atmosphere revealed by Machine Learning enhanced Global Optimization

*Thanh Ngoc Pham¹, Beatriz A.C. Tan¹, Yuji Hamamoto^1,2, Kouji Inagaki^1,2, Ikutaro Hamada^1,2, Yoshitada Morikawa^1,2,3 (1. Department of Precision Engineering, Graduate School of Engineering, Osaka University, 2-1, Yamada-oka, Suita, Osaka 565-0871, Japan (Japan), 2. Elements Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Goryo-Ohara, Nishikyo-ku, Kyoto 615-8245, Japan (Japan), 3. Research Center for Ultra-Precision Science and Technology, Graduate School of Engineering, Osaka University, 2-1 Yamada-oka, Suita 565-0871, Osaka, Japan (Japan))

キーワード：sintering, density functional theory, machine learning, global optimization, metal-support interaction