Session Details
[20p-C601-1~16]23.1 Joint Session N "Informatics"
Wed. Sep 20, 2023 1:30 PM - 6:00 PM JST
Wed. Sep 20, 2023 4:30 AM - 9:00 AM UTC
Wed. Sep 20, 2023 4:30 AM - 9:00 AM UTC
C601 (Int'l Ctr.)
Masato Kotsugi(Tokyo Univ. of Sci.), Yuma Iwasaki(NIMS), Kenji Nagata(NIMS)
[20p-C601-1]Exploration of undiscovered aqueous dispersants for carbon nanotube using machine learning
〇Hirokuni Jintoku1, Don Futaba1 (1.AIST)
[20p-C601-2]Feature Extraction of Machine Learning for Prediction of MOCVD-grown GaN surface
〇Tsutomu Sonoda1, Tokio Takahashi1, Yosuke Tsunooka2, Masaki Takaishi3, Shota Seki2,3, Kentaro Kutsukake4, Toshihide Ide1, Mitsuaki Shimizu1, Toru Ujihara2 (1.NU-AIST GaN-OIL, 2.Grad. School of Eng. Nagoya Univ., 3.AIxtal, 4.AIP RIKEN)
[20p-C601-3]Construction of a Machine Learning based Digital-twin for the MOVPE of GaN
〇Shota Seki1,2, Yuka Hashizume1, Masaki Takaishi1, Yosuke Tsunooka2, Kentaro Kutsukake4, Tsutomu Sonoda3, Tokio Takahashi3, Toshihide Ide3, Mitsuaki Shimizu3, Toru Ujihara2 (1.AIxtal, 2.Nagoya Univ., 3.NU-AIST GaN-OIL, 4.AIP RIKEN)
[20p-C601-4]Development of automated system for solid-state alloys synthesis
〇Kensei Terashima1, Wei-Sheng Wang1,2, Yoshihiko Takano1,2 (1.NIMS, 2.Univ. of Tsukuba)
[20p-C601-5]Asynchronous and Distributed Control Lab System for automated solid materials synthesis
〇(D)WeiSheng Wang1,2, Kensei Terashima1, Yoshihiko Takano1,2 (1.NIMS, 2.Univ. of Tsukuba)
[20p-C601-6]Investigation of the Mechanochemical Reaction Process by Automated Measurement
〇Yusaku Nakajima1, Takafumi Hawai1, Yasuo Takeichi1, Yuto Yotsumoto1, Ryusei Takamoto1, Kanta Ono1 (1.Osaka Univ.)
[20p-C601-7][Young Scientist Presentation Award Speech] Development of real-time and high-speed magnetic domain measurement system for iron loss analysis and Application of machine learning
〇Ryunosuke Nagaoka1, Ken Masuzawa1, Alexandre Lira Foggiatto1, Chiharu Mitsumata1, Takahiro Yamazaki1, Ippei Obayashi2, Yasuaki Hiraoka3, Masato Kotsugi1 (1.Tokyo Univ. of Science, 2.Okayama Univ., 3.Kyoto Univ.)
[20p-C601-8]Multimodal Deep Learning for Predicting Diverse Properties pf Functional Materials and Elucidation of Pareto Frontiers
〇Shun Muroga1, Yasuaki Miki1, Kenji Hata1 (1.AIST)
[20p-C601-9]Prediction of transport properties of polycrystalline superconducting materials based on deep learning of microstructual images
〇Tatsunori Ishibashi1, Takahiro Hosokawa1, Yoshiki Nishiya1, Yu Hirabayashi1, Haruka Iga1, Takuya Obara1, Motomune Kodama2, Hideki Tanaka2, Akiyasu Yamamoto1 (1.Tokyo Univ. Agri. & Tech., 2.Hitachi Ltd.)
[20p-C601-10]An AI system for big data generation of composite materials and their structural optimization
〇Hiroshi Ishikawa1, Hikaru Yoshizawa1 (1.FUJIFILM)
[20p-C601-11]X-ray Phase Sparse-View CT of CFRP fibers using Deep Learning
〇MIdori Taguri1, Naoki Morimoto2, Keishi Kitamura1,2 (1.Nara Inst. of Sci. & Tech., 2.Shimadzu Corp.)
[20p-C601-12]Adaptive Experimental Design for Model Selection and its Parameter Estimation
〇(M2)Tomohiro Nabika1, Kenji Nagata2, Shun Katakami1, Mizumaki Masaichiro3, Masato Okada1 (1.Univ. of Tokyo, 2.NIMS, 3.Kumamoto Univ.)
[20p-C601-13]Development of a featurization tool for measurement spectra using reference data
〇Ryo Murakami1, Kenji Nagata1, Hideki Yoshikawa1 (1.NIMS)
[20p-C601-14]Fully automated high-speed analysis of spectroscopic ellipsometry by deep learning: application of large-scale spectral data
〇Ryosuke Oka1, Masahiro Hayashi1, Hiroyuki Fujiwara1 (1.Gifu Univ.)
[20p-C601-15]Uncertainty Quantification of X-ray Absorption Spectra and Application to Measurement Optimization
〇(M1)Yusei Ito1, Yasuo Takeichi1, Hideitsu Hino2, Kanta Ono1 (1.Osaka Univ., 2.ISM)
[20p-C601-16]Cross-facility database for X-ray absorption spectroscopy
〇Masashi Ishii1, Takahiro Matsumoto2, Yasuhiro Inada3, Masao Kimura4, Masao Tabuchi5,6, Eiichi Kobayashi7, Kiyotaka Asakura8 (1.NIMS, 2.JASRI, 3.Ritsumeikan-SR, 4.KEK, 5.Nagoya Univ., 6.Aichi-SR, 7.SAGA-LS, 8.Hokkaido Univ.)