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SCEJ 54th Autumn Meeting (Fukuoka, 2023)

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

Hall and day program : Hall H, Day 1 : H102

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

TimePaper
ID
Title / AuthorsKeywordsTopic codeAck.
number
ST-21 [Trans-Division Symposium]
Frontiers of Data-driven Research and Development
(9:00–10:40) (Chair: Shimada Iori)
9:009:20H101Development 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-21445
9:209:40H102Predicting Physical Properties of Structurally Unknown Polymers Using Spectroscopy Data
(Resonac) (Cor)Nagai Yuuki
Machine Learning
Predict
Descriptor
ST-21353
9:4010:00H103(withdrawn)

100364
10:0010:20H104[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-21602
10:2010:40H105Construction of MI platform for functional materials
(Resonac) (Cor)Sekiguchi Kazuhide
Materials informatics
DX
ST-21450
(10:40–11:20) (Chair: Kaneko Shogo)
10:4011:20H106[Invited lecture] Material exploration and process optimization by digital technology
(NAIST) Fujii Mikiya
Materials Informatics
Process Informatics
Quantum Chemistry
ST-21784
(11:20–12:00) (Chair: Mukaida Shiho)
11:2012:00H108[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-21976
(13:00–13:40) (Chair: Mukaida Shiho)
13:0013:40H113[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-21979
(13:40–15:20) (Chair: Toya Yoshihiro)
13:4014:20H115[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-21805
14:2014:40H117Deep learning model for predicting all protein-protein interactions from sequence data
(Kyutech) *(Reg)Kurata Hiroyuki, Tsukiyama Sho
Cross attention
deep learning
prediction
ST-2133
14:4015:00Break
15:0015:20H119Development 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-21728
(15:20–17:00) (Chair: Muroga Shun)
15:2015:40H120Development 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-21225
15:4016:00H121Machine 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-21235
16:0016:20H122High 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-21686
16:2016:40H123Application 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-21480
16:4017:00H124Applicational 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-21948

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SCEJ 54th Autumn Meeting (Fukuoka, 2023)


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