Session Details
[19p-C601-1~15]23.1 Joint Session N "Informatics"
Tue. Sep 19, 2023 1:30 PM - 5:45 PM JST
Tue. Sep 19, 2023 4:30 AM - 8:45 AM UTC
Tue. Sep 19, 2023 4:30 AM - 8:45 AM UTC
C601 (Int'l Ctr.)
Kentaro Kutsukake(RIKEN), Yasuhiko Igarashi(Tsukuba Univ.), Shun Muroga(AIST)
[19p-C601-1]Neural network-based simulation method to examine ion behaviors under external electric fields: applications to crystalline and amorphous Li3PO4
〇Koji Shimizu1, Ryuji Otsuka1, Satoshi Watanabe1 (1.UTokyo)
[19p-C601-2]Equivariant Neural Networks for Spin Dynamics Simulations of Itinerant Magnets
〇(D)Yu Miyazaki1 (1.Univ. Tokyo)
[19p-C601-3]Structural Design Principles for Superionic Conductors by Semi-Supervised Learning
〇Tomoyasu Yokoyama1, Kazuhide Ichikawa1, Takuya Naruse1, Kosei Ohura1, Yukihiro Kaneko1 (1.Panasonic Holdings Corp.)
[19p-C601-4]Extraction of polymer refractive index laws
by symbolic regression using bayesian information criterion
〇Naoki Yamane1, Kan Hatakeyama2, Yuma Iwasaki3, Yasuhiko Igarash1 (1.Univ. of Tsukuba, 2.Tokyo Inst. of Tech., 3.NIMS)
[19p-C601-5]High-precision prediction of band gap by the application of a three-dimensional convolutional neural network to spatial charge distribution information
〇Masahiro Hayashi1, Ryosuke Oka1, Ryoki Toda1, Ryo Ishihara1, Hiroyuki Fujiwara1 (1.Gifu Univ.)
[19p-C601-6]Text mining and machine learning of Eu valence states in phosphor materials
〇Yukinori Koyama1, Naoto Hirosaki1, Yukako Kohriki1, Takashi Takeda1 (1.NIMS)
[19p-C601-7]New material search maps corresponding to physical properties by machine learning
〇Yuki Inada1, Erina Fujita2, Kaoru Kimura2, Yukari Katsura2 (1.Univ. of Tokyo, 2.NIMS)
[19p-C601-8]Visual Topic Mapping for Battery Related Research using a Global Open Catalog
〇Sae Dieb1, Luca Foppiano1, Keitaro Sodeyama1, Mikiko Tanifuji2 (1.CBRM, National Institute for Materials Science, Japan, 2.RCOS, National Institute of Informatics, Japan)
[19p-C601-9]Question-answering-based approach for mining information from documents using Large Language Models
〇Luca Foppiano1,2, Guillaume Lambard1, Toshiyuki Amagasa2, Masashi Ishii1 (1.Data-driven Materials Design Group, CBRM, NIMS, 2.KDE, CCS, Univ. of Tsukuba)
[19p-C601-10]Enhancing Inverse Problem Solutions with Accurate Surrogate Simulators and Promising Candidates
〇Akihiro Fujii1, Hideki Tsunashima2, Yoshihiro Fukuhara2,3, Koji Shimizu1, Satoshi Watanabe1 (1.Tokyo Univ., 2.Waseda Univ., 3.ExaWizards Inc.)
[19p-C601-11]Accelerated Autonomous Material Searching through Transfer Learning from Nontargeted Properties
〇(P)Jaekyun Hwang1, Yuma Iwasaki1 (1.NIMS)
[19p-C601-12]Bayesian optimization framework for high ionic conductivity material exploration
〇(DC)Yuki Sakishita1, Yibin Xu2, Koji Hukushima1,3 (1.Basic Sci., Univ. of Tokyo, 2.NIMS, 3.Komaba Inst. for Sci., Univ. of Tokyo)
[19p-C601-13]Quantum annealing and ab-initio calculations for optimizing cation-disordered oxides
〇Kenji Nawa1,2, Tsuyoshi Suzuki3, Keisuke Masuda2, Shu Tanaka4,5, Yoshio Miura2,6 (1.Mie Univ., 2.NIMS, 3.TDK Corp., 4.Keio Univ., 5.WPI-Bio2Q, Keio Univ., 6.CSRN, Osaka Univ.)
[19p-C601-14]Simulating Sintering of Solid Material by Ising Machine
〇Noriaki Ozaki1, Jun Ikeda1 (1.Murata Mfg.)
[19p-C601-15]Fast data generation for conductivity with vectorized Kubo formula
〇Yuta Yahagi1,2, Toshihiro Kato1 (1.NEC, 2.AIST)