ST-21 - Session programs - SCEJ 56th Autumn Meeting (Tokyo, 2025)

`SCEJ 56th Autumn Meeting (Tokyo, 2025)`

Last modified: 2025-09-29 10:33:36

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Session programs : ST-21

The preprints(abstracts) are now open. These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants (excludes free registration) and invited persons are required.

ST-21 [Trans-Division Symposium]
Frontiers of Data-driven Research and Development

Organizers:

Shimada Iori (Shinshu Univ.), Kim Sanghong (TUAT), Toya Yoshihiro (Osaka Univ.), Yoshida Hideaki (Sumitomo Chemical), Mukaida Shiho (Mitsui Chemicals), Muroga Shun (AIST), Sugawara Yuuki (Science Tokyo), Sugisawa Hiroki (Mitsubishi Chemical), Xia Junqing (Fukuoka Univ.)

Data science has been rapidly developing in recent years as the fourth science following experimental science, theoretical science, and computational science. In the field of chemical engineering, data-driven science, which derives superior materials and processes by making full use of a large amount of accumulated data and information, is becoming increasingly important, and many efforts are being made. This symposium will have speakers who are making pioneering efforts toward a data-driven society from various viewpoints and discuss future research and development.

Hall CD, Day 1 | Hall CD, Day 2

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Hall CD(C.A. Bldg. 3F 301), Day 1(Sep. 16)
(9:20–10:20) (Chair: Shimada Iori)
9:20– 9:40	CD102	High-resolution flow analysis in cell culture tanks using a combination of PINNs and CFD (Akita U.) *(Reg)Horiguchi Ikki, (U. Osaka) Shima Keisuke, (Stu)Mizukami Yuto, (Reg)Okano Yasunori	Suspension culture Computational flow dynamics Neural Networks	ST-21	171
9:40– 10:00	CD103	Prediction of electrostatic inkjet printing characteristics using CNN with electric field distribution as input (Tokyo U. Sci.) *(Reg)Matsukawa Hiroaki, (Reg)Otake Katsuto	Electrostatic inkjet Convolutional Neural Networks Electric field distribution	ST-21	97
10:00– 10:20	CD104	Novel feature suggestion for Predictive Model of the Bacterial Reverse Mutation Test (Kogakuin U.) *(Stu)Kondo Kazuma, (Stu)Miyatake Koshiro, (Reg)Higuchi Hayato, (Reg)Miyagawa Masaya, (Reg)Takaba Hiromitsu	Machine Learning Variational Autoencoder COSMO method	ST-21	744
10:20– 10:40		Break
(10:40–12:00) (Chair: Muroga Shun)
10:40– 11:20	CD106	[Invited lecture] Practical studies of data-driven materials research based on accelerating the data cycle (Science Tokyo) Ando Yasunobu	autonomous experiment machine learning potential spectral analysis	ST-21	939
11:20– 12:00	CD108	[Invited lecture] Accelerating Catalyst Development through High-Throughput Experiments (AIST) Fujitani Tadahiro	Catalyst development High-throughput screening Autonomous experimentation	ST-21	935
(13:00–14:20) (Chair: Mukaida Shiho)
13:00– 13:40	CD113	[Invited lecture] Machine learning-enabled spectroscopy and materials discovery (U. Tokyo) Mizoguchi Teruyasu	Machine learning XANES/ELNES Materials discovery	ST-21	938
13:40– 14:20	CD115	[Invited lecture] Optimization Applications in Business and Information Systems (NS Solutions) Minami Etsuro	Optimization Problems Combinatorial Optimization Business Applications	ST-21	946
(14:20–16:20) (Chair: Toya Yoshihiro)
14:20– 15:00	CD117	[Invited lecture] Perspectives and Challenges in Laboratory Automation Supporting Data-Driven Science in the field of Biotechnology (Riken) Horinouchi Takaaki	Laboratory automation Biotechnology Data-Driven Science	ST-21	945
15:00– 15:20		Break
15:20– 15:40	CD120	Development of automated and autonomous experimental systems for electrode slurry: challenges and approaches (Toyota Central R&D Labs) *(Cor)Kudo Sayako, (Cor)Matsunaga Takuro, (Cor)Makino Soichiro, (Cor)Kusano Takumi, (Cor)Yamawaki Yuya, (Reg)Nakamura Hiroshi, (U. Tokyo) (Stu)Oya Hirotaka, (Reg)Nagato Keisuke	automatic and autonomous laboratory slurry viscosity	ST-21	346
15:40– 16:00	CD121	A Heterogeneous Transfer Learning Approach for Manufacturing Method Transitions in Pharmaceutical Processes (Kyoto U.) *(Stu)Ihira Junya, (Daiichi Sankyo) (Reg)Yaginuma Keita, (Cor)Sato Kanta, (Kyoto U.) (Reg)Kato Shota, (Reg)Kano Manabu	Heterogeneous Transfer Learning Manufacturing Method Transition Pharmaceutical Manufacturing	ST-21	471
16:00– 16:20	CD122	Development of a method for predicting drug-drug interactions using food ingredients as substitute for negative data (Meiji U.) *(Stu)Kosakai Soma, (Reg)Kaneko Hiromasa	Drug-drug interaction Machine learning Positive-unlabeled learning	ST-21	178
Hall CD(C.A. Bldg. 3F 301), Day 2(Sep. 17)
(9:00–10:20) (Chair: Sugawara Yuuki)
9:00– 9:20	CD201	Improvement of reaction mechanism prediction by chemical reaction neural network and its extension to heterogeneous catalytic reaction systems (Shinshu U.) *(Reg)Shimada Iori, Yokosuka Natsuki, Nakajima Riku, Shionoya Tomoki	chemical reaction neural network kinetic model heterogeneous catalyst	ST-21	923
9:20– 9:40	CD202	Machine Learning for Flow Suzuki Coupling Reactions Using Immobilized Pd Catalysts (Kyushu U.) *(Stu)Zhou Xincheng, (Reg)Matsumoto Hikaru, (Reg)Nagao Masanori, (Reg)Miura Yoshiko	Polymer immobilized Pd catalyst Machine learning Process optimization	ST-21	469
9:40– 10:00	CD203	Development of uncertainty quantification methods using sequential Monte Carlo methods and demonstration in reactor modeling (Nagoya U.) *(Stu)Maruchi Tatsuki, (Reg)Yajima Tomoyuki, (Reg)Kawajiri Yoshiaki	Sequential Monte Carlo Uncertainty quantification Methanation	ST-21	763
10:00– 10:20	CD204	Applications of transfer learning in catalyst and polymer development (NEC/AIST) *(Reg)Obuchi Kiichi, Yahagi Yuta, (AIST) (Reg)Kosaka Fumihiko, (Kyoto U.) Matsui Kota	transfer learning machine learning data driven approach	ST-21	746
10:20– 10:40		Break
(10:40–12:00) (Chair: Xia Junqing)
10:40– 11:00	CD206	Machine Learning-Aided Prediction of the Lower Critical Solution Temperature of Thermoresponsive Copolymers (Science Tokyo) (Reg)Sugawara Yuuki	Thermoresponsive polymer Lower critical solution temperature Machine learning	ST-21	31
11:00– 11:20	CD207	(withdrawn)		100	527
11:20– 11:40	CD208	High-Accuracy Building Electricity Demand Forecasting Using Group Encoding for High-Dimensional Binary Data (Science Tokyo) *(Stu)Kagawa Tatsuya, (Stu)Lee Hyojae, (Reg)Kameda Keisuke, (Reg)Manzhos Sergei, (Reg)Ihara Manabu	group encoding demand forecasting big data	ST-21	936
11:40– 12:00	CD209	Prediction of ultrafiltration membrane fouling and optimization of operating conditions using machine learning models (Meiji U.) *(Stu)Shino Yuto, (Hitachi Plant Services) Terui Shigeki, (Meiji U.) (Reg)Kaneko Hiromasa	Machine Learning Ultrafiltration Membrane Fouling	ST-21	235

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SCEJ 56th Autumn Meeting (Tokyo, 2025)

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SCEJ 56th Autumn Meeting (Tokyo, 2025)

Session programs : ST-21

ST-21 [Trans-Division Symposium] Frontiers of Data-driven Research and Development

`SCEJ 56th Autumn Meeting (Tokyo, 2025)`

ST-21 [Trans-Division Symposium]
Frontiers of Data-driven Research and Development