A113 - Hall A, Day 1 - Hall and day program - SCEJ 57th Autumn Meeting (Higashihiroshima, 2026)

`SCEJ 57th Autumn Meeting (Higashihiroshima, 2026)`

Last modified: 2026-07-16 06:38:20

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Hall and day program : Hall A, Day 1 : A113

The chairs are under negotiation.
Yellow-backs on Technical sessions denote the Hybrid sessions.
(All other sessions are on-site only sessions).

Hall A(K1F K108), Day 1(Sep. 14)

ST-21

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
ST-21 [Trans-Division Symposium] Frontiers of Data-driven Research and Development
(10:00–11:20)
10:00– 10:20	A104	Development of a Machine Learning Model for Predicting Solubility in Binary Solvent Systems at Arbitrary Temperatures (Meiji U.) *(Stu)Watanabe Hibiki, (Reg)Kaneko Hiromasa	Chemoinformatics Solubility prediction Machine learning	ST-21	104
10:20– 10:40	A105	A data-driven framework for identifying critical solvent parameters in hydrogenation reactions (AIST) *(Reg)Febi Yusniyanti, (Reg)Benny Wahyudianto, Saito Takashi, (Reg)Kataoka Sho	Machine learning Hydrogenation Solvent effect	ST-21	778
10:40– 11:00	A106	Optimal Design of e-Methanol Production Process Using Machine Learning and Superstructure Optimization (Meiji U.) *(Stu)Sato Takao, (Reg)Kaneko Hiromasa	e-methanol Machine learning Process design	ST-21	301
11:00– 11:20	A107	Verification and Evaluation of Large Language Models for Supporting Chemical Process Design (Kyoto U.) *(Reg)Takahara Wataru, Suganuma Ryu, (Reg)Miyamoto Souta, Tanabe Katsuaki	Large Language Models Chemical Process Benchmark	ST-21	481
(11:20–12:00)
11:20– 12:00	A108	[Invited lecture] Hybrid modeling for connecting experimental data with process design (U. Tokyo) (Reg)Sugiyama Hirokazu	hybrid modeling pharmaceutical manufacturing design space	ST-21	920
(13:00–13:40)
13:00– 13:40	A113	[Invited lecture] The Frontline of AI and Autonomous Experimentation for Winning the Competition (Asahi Kasei) (Cor)Tsukumo Hiroshi	AI agent Autonomous experiment Materials informatics	ST-21	921
(13:40–15:00)
13:40– 14:00	A115	Sequential Processing of Long Texts Using Large Language Models for Literature Mining of Endothelial Cell Culture Protocols (UTokyo) *(Stu)Ito Shusuke, Shioda Hiroki, (Stu)Shimodaira Gaku, (Reg)Nishikawa Masaki, (Reg)Sakai Yasuyuki, (Reg)Muraoka Koki	LLM Text mining Endothelial cell	ST-21	791
14:00– 14:20	A116	*Construction of machine learning models for predicting in vivo* responses of artificial bone materials using in vitro indicators** (Meiji U.) *(Stu)Masuyama Naoki, Iwama Shoki, Aizawa Mamoru, (Reg)Kaneko Hiromasa	artificial bone in vivo in vitro	ST-21	112
14:20– 14:40	A117	Construction of a miRNA-mRNA Binding Prediction Model Using Unknown Interaction Data and Performance Evaluation with Non-binding Data (Meiji U.) *(Stu)Endo Shion, (Stu)Ishikawa Eri, (U. Tokyo) (Reg)Nakamura Noriko, (Reg)Ohta Seiichi, (Meiji U.) (Reg)Kaneko Hiromasa	Machine Learning bioinformatics RNA	ST-21	305
14:40– 15:00	A118	Development of an instability analysis for freezing process changes in cell-based products (U. Osaka) *(Reg)Uno Yuki, (Stu)Tanaka Koichi, (UTokyo) (Reg)Hayashi Yusuke, (Reg)Sugiyama Hirokazu, (Iwatani) Nakamura Tetsuji, (U. Osaka) (Reg)Kino-oka Masahiro	Instability analysis Freezing process change Cell-based product	ST-21	578
(15:20–16:40)
15:20– 15:40	A120	Automated MOF Synthesis Using a Robotic Arm and an Electric Pipette (Tohoku U. FRIS) *(Reg)Hashimoto Yusuke, (Tohoku U.) (Stu·PCEF)Muramoto Takaya, Terada Hikari, Harim Song, Shimpo Aoi, (Int)Yuan Wang, (Tohoku U. FRIS) (Reg)Tomai Takaaki	Metal-organic framework Laboratory automation Particle size control	ST-21	450
15:40– 16:00	A121	Advancing Chemical Engineering Research through an Integrated AI Platform (Aizoth) *(Reg)Rajapriya Navin, (Cor)Kawajiri Kotaro	AI Cheminformatics Optimization	ST-21	635
16:00– 16:20	A122	Development and evaluation using novel chemical feature-augmented GNN (Kogakuin U.) *(Stu)Kondo Kazuma, (Reg)Higuchi Hayato, (Reg)Miyagawa Masaya, (Reg)Takaba Hiromitsu	machine learning molecular graph coarse graph	ST-21	754
16:20– 16:40	A123	Fast estimation of H⁺ conductive barriers in perovskite oxides using the bond valence approach with machine learning (Science Tokyo) *(Stu)Amo Masaki, Ito Kazuma, (Reg)Kameda Keisuke, (Reg)Manzhos Sergei, (Reg)Ihara Manabu	H+ conductive oxides Machine learning Solid oxide fuel cell	ST-21	474

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SCEJ 57th Autumn Meeting (Higashihiroshima, 2026)

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