Hall H, Day 1 - Hall and day program - SCEJ 54th Autumn Meeting (Fukuoka, 2023)

`SCEJ 54th Autumn Meeting (Fukuoka, 2023)`

Last modified: 2023-12-10 19:09:26

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Hall and day program : Hall H, Day 1

The preprints(abstracts) are now open (Aug. 28). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants and invited persons are required.

Hall H(835), Day 1(Sep. 11)

ST-21

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
ST-21 [Trans-Division Symposium] Frontiers of Data-driven Research and Development
(9:00–10:40) (Chair: Shimada Iori)
9:00– 9:20	H101	Development of machine learning model for CO₂ absorption performance of blended amine solutions (AIST) *(Reg)Fujii Tatsuya, (Reg)Kohno Yuki, (Reg)Makino Takashi, (Tokyo Tech) Sako Masami, Ishihama Keisuke, Yasuo Nobuaki, Kawauchi Susumu	CO2 absorption machine learning amine	ST-21	445
9:20– 9:40	H102	Predicting Physical Properties of Structurally Unknown Polymers Using Spectroscopy Data (Resonac) (Cor)Nagai Yuuki	Machine Learning Predict Descriptor	ST-21	353
9:40– 10:00	H103	(withdrawn)		100	364
10:00– 10:20	H104	[Featured presentation] Multimodal Deep Learning for Predictions of Various Properties of Composite Materials (AIST) *(Reg·PCEF)Muroga Shun, Miki Yasuaki, Hata Kenji	multimodal deep learning materials informatics generative deep learning	ST-21	602
10:20– 10:40	H105	Construction of MI platform for functional materials (Resonac) (Cor)Sekiguchi Kazuhide	Materials informatics DX	ST-21	450
(10:40–11:20) (Chair: Kaneko Shogo)
10:40– 11:20	H106	[Invited lecture] Material exploration and process optimization by digital technology (NAIST) Fujii Mikiya	Materials Informatics Process Informatics Quantum Chemistry	ST-21	784
(11:20–12:00) (Chair: Mukaida Shiho)
11:20– 12:00	H108	[Invited lecture] Data-driven Approaches for Functional Materials Development in SEKISUI CHEMICAL. (Sekisui Chemical) (Cor)Masuyama Yoshikazu	Data-Driven Development Functional Materials Materials Informatics	ST-21	976
(13:00–13:40) (Chair: Mukaida Shiho)
13:00– 13:40	H113	[Invited lecture] Remote Operation Support and Automatic Plant Operation Technology In Waste-to-Energy Plants (JFE Eng.) (Cor)Kojima Hiroshi	Remote operation Automatic operation AI and Data analysis	ST-21	979
(13:40–15:20) (Chair: Toya Yoshihiro)
13:40– 14:20	H115	[Invited lecture] Prediction and control of bacterial evolution through high-throughput automated experiments using robots (RIKEN) *Shibai Atsushi, Furusawa Chikara	Laboratory automation Laboratory evolution Escherichia coli	ST-21	805
14:20– 14:40	H117	Deep learning model for predicting all protein-protein interactions from sequence data (Kyutech) *(Reg)Kurata Hiroyuki, Tsukiyama Sho	Cross attention deep learning prediction	ST-21	33
14:40– 15:00		Break
15:00– 15:20	H119	Development of mechanistic cell cultivation models in monoclonal antibody production using data-driven insights (UTokyo) *(Stu)Okamura K., (Int)Badr S., (Stu)Ichida Y., (Reg·SPCE)Yamada A., (Reg)Sugiyama H.	Biopharmaceuticals Lactate consumption Glutamine	ST-21	728
(15:20–17:00) (Chair: Muroga Shun)
15:20– 15:40	H120	Development of microbial production process by model based metabolic design and directed evolution (Osaka U.) *(Reg)Shimizu Hiroshi, (Reg)Toya Yoshihiro, (RIKEN) Furusawa Chikara, Shibai Atsushi, (AIST) Horinouchi Takaaki, (Chuo U.) Suzuki Hiroaki, (Osaka U.) Tokuyama Kento, (Reg)Niide Teppei	Model based metabolic pathway design Directed evolution Metabolic engineering	ST-21	225
15:40– 16:00	H121	Machine learning guided enzyme’s molecular recognition specificity conversion (Osaka U.) *(Reg)Niide Teppei, Sugiki Sou, Mori Seiya, (Reg)Toya Yoshihiro, (Reg)Shimizu Hiroshi	enzyme design machine learning	ST-21	235
16:00– 16:20	H122	High accuracy prediction of edible oil oxidation stability by multivariate analysis incorporating chemiluminescence information (Tohoku U.) *(Stu·PCEF)Yoshida Yuta, (Reg)Hiromori Kousuke, (Reg)Shibasaki-Kitakawa Naomi, (Reg)Takahashi Atsushi	multivariate analysis oxidative stability edible oil	ST-21	686
16:20– 16:40	H123	Application of reaction mechanism search method using chemical reaction neural network to glycerol oxidation reaction (Shinshu U.) *(Stu)Shionoya Tomoki, (Reg)Shimada Iori	physics informed neural network kinetics model data-driven	ST-21	480
16:40– 17:00	H124	Applicational study of symbolic regression to exploring new materials and constructing kinetics models (Waseda U.) *(Stu)Isoda T., Takahashi S., (WISE/Mitsubishi Chemical Group) Nakano M., (WISE) Nakajima Y., (Waseda U./WISE) Seino J.	Machine learning Materials Informatics Reaction Kinetics	ST-21	948

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SCEJ 54th Autumn Meeting (Fukuoka, 2023)

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