Session Details
[9a-N304-1~11]18.1 Materials Informatics
Wed. Sep 9, 2026 9:00 AM - 12:00 PM JST
Wed. Sep 9, 2026 12:00 AM - 3:00 AM UTC
Wed. Sep 9, 2026 12:00 AM - 3:00 AM UTC
N304 (First Year Education Bld. N Block)
[9a-N304-1]Local Structure and Bond-Order Distribution of Liquid Si Using the Tersoff-NN Potential
〇(DC)Yusuke Nishimura1, Sho Kubota1, Watanabe Takanobu1 (1.Waseda Univ.)
[9a-N304-2]Assessment of the Hybrid Machine-Learning Potential “Tersoff-NN” for Si Surface Structure
〇(M2)Sho Kubota1, Yusuke Nishimura1, Takanobu Watanabe1 (1.Waseda Univ.)
[9a-N304-3]Prediction of Dielectric Constants of CaTiO3 Using Δ-Learning Model with HSE
〇(M2)Koki Yoshimochi1, Alex Kutana1, Ryosuke Jinnouchi1, Ryouji Asahi1 (1.Nagoya Univ.)
[9a-N304-4]Local Structure Prediction and Charge–Discharge Stability Analysis of Fluoride-Ion Cathode Material SrFeO2Fx
〇(M1)Subaru Shibusawa1, Alex Kutana1, Ryoji Asahi1 (1.Nagoya Univ.)
[9a-N304-5]A Machine Learning Compatible Workflow to Optimize Free Energy Models using Experimental Phase Equilibria Data
〇(P)Wenhao Zhang1, Yusuke Matsuoka1, Taichi Abe1 (1.NIMS)
[9a-N304-6]Electric-Field-Induced Switching Behavior in Antiferroelectric PbZrO3 via Machine Learning Potential Molecular Dynamics
〇ChihLun Hsu1, Ryotaro Sahashi1, Po-Yen Chen1, Teruyasu Mizoguchi1,2 (1.Univ. of Tokyo Eng., 2.Univ. of Tokyo IIS)
[9a-N304-7][The 60th Young Scientist Presentation Award Speech] Functional Dependence of Machine Learning Force Fields in Electric-Field-Induced Molecular Dynamics of BaTiO3
〇Ryotaro Sahashi1, Po-Yen Chen1, Teruyasu Mizoguchi1,2 (1.Univ. of Tokyo Eng, 2.Univ. of Tokyo IIS)
[9a-N304-8]Free Energy Analysis of HF Dissociation in Aqueous Solution Using a Machine Learning Interatomic Potential
〇Toshihiro Kume1,2, Rizka Nur Fadilla1, Harry Handoko Halim1, Yoshitada Morikawa1 (1.Osaka Univ., 2.SCREEN)
[9a-N304-9]Deep Potential molecular dynamics analysis of surface enrichment of boroxol rings in molten B2O3
〇Jun Otsuka1 (1.Sumitomo Electric Industries, Ltd.)
[9a-N304-10]Atom-Resolved Electromechanics of Single-Domain and Domain-Wall Switching in Bilayer Hexagonal Boron Nitride via Deep Learning Potentials
〇Yinan Wang1,2, Poyen Chen1,2, Mizoguchi Teruyasu1 (1.IIS. U-Tokyo, 2.Eng. U-Tokyo)
[9a-N304-11]Structure Screening of Ionic Solid Solutions Using Universal MLIPs
〇(D)Taku Sakai1, Tom Ichibha1, Ryo Maezono2, Kenta Hongo3 (1.JAIST, 2.Science Tokyo, 3.JAIST CASC)
