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SCEJ 53rd Autumn Meeting (Nagano, 2022)

Last modified: 2022-12-14 13:12:56

Session programs : ST-21

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

Organizers: Shimada Iori (Shinshu Univ.), Kim Sanghong (Tokyo Univ. of Agri. and Tech.), Toya Yoshihiro (Osaka Univ.), Kaneko Shogo (Sumitomo Chemical), Mukaida Shiho (Mitsui Chemicals)

Data science has been rapidly developing in recent years as the fourth science following experimental science, theoretical science, and computational science. The early realization of a data-driven society led by data science has been recognized as a key to international competitiveness. 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 DC, Day 3

Title / AuthorsKeywordsTopic codeAck.
Hall DC(C3 1F 102), Day 3(Sep. 16)
(9:00–10:20) (Chair: Mukaida Shiho)
9:009:40DC301[Invited lecture] Data-driven AI Laboratory and Cyber Catalysis
(Shinshu U.) (Reg)Koyama Michihisa
Cyber Catalysis
Computational Chemistry
9:4010:20DC303[Invited lecture] AI-Driven peptide/antibody molecule design for drug discovery
(MOLCURE) Tamaki Satoshi
Artificial Intelligence
Drug Discovery
(10:20–12:00) (Chair: Toya Yoshihiro)
10:2010:40DC305Data-driven analysis of charge variants in monoclonal antibody production
(U. Tokyo) *(Stu)Yoshiyama Yuki, (Int)Badr Sara, (Stu)Okamura Kozue, (Manufacturing Tech. Association of Biologics) Murakami Sei, (U. Tokyo) (Reg)Sugiyama Hirokazu
Charge variant
Monoclonal antibody
10:4011:00DC306Multi-step approach for data-driven equipment condition assessment in biopharmaceutical drug product manufacturing
(U. Tokyo) *(Int)Zuercher Philipp Samuel, (Int)Badr Sara, (ROCHE) Knueppel Stephanie, (U. Tokyo) (Reg)Sugiyama Hirokazu
Predictive maintenance
Unsupervised learning
Industrial application
11:0011:20DC307Reinforcement learning to optimally control the bio and chemical processes
(Kyoto U.) (Int)Oh Tae Hoon
Reinforcement Learning
Process control
Optimal control
11:2011:40DC308Soft sensor study in film manufacturing process
(Meiji U.) *(Stu)Nakayama Yuki, Shiraki Yuya, (Zeon) (Cor)Natori Satoshi, (Cor)Ono Yuki, (Cor)Suda Kazuya, (Meiji U.) (Reg)Kaneko Hiromasa
Soft sensor
Fault detection
Film manufacturing process
11:4012:00DC309Prediction of phase equilibrium of water-organic compounds system at high-temperature and high-pressure using machine learning
(Shinshu U.) *(Stu)Tamura Kotaro, (Reg)Shimada Iori, (Reg)Fukunaga Hiroshi, (Reg)Takahashi Nobuhide, (Reg)Osada Mitsumasa
machine learning
prediction of phase equilibrium
high-temperature and high-pressure
(13:00–14:20) (Chair: Kaneko Shogo)
13:0013:40DC313[Invited lecture] Exploration of functional inorganic thin-film materials using autonomous systems
(Tokyo Tech) Shimizu Ryota
autonomous synthesis
inorganic materials
functional thin films
13:4014:20DC315[Invited lecture] Data-driven polymer material development powered by Polymer SmartLab and Material DX
(NIMS) Naito Masanobu
smart lab
material DX
(14:20–15:40) (Chair: Shimada Iori)
14:2014:40DC317Inverse design of polymer membrane structure for gas separation using Junction Tree VAE machine learning
(Kogakuin U.) *(Stu)Matsumoto Takumi, (Reg)Miyagawa Masaya, (Reg)Takaba Hiromitsu
machine learning
polymer membrane
gas separation
14:4015:00DC318Design of both membrane-based process and membrane materials with machine learning
(Meiji U.) *(Stu)Yuyama Shunsuke, (Reg)Kaneko Hiromasa
Membrane module
Materials Informatics
Process design
15:0015:20DC319[Featured presentation] Development of digital twin of the bulk single crystal growth of Si by using PINNs (Physics Informed Neural Networks)
(Osaka U.) *(Stu)Takehara Yuto, (Reg)Okano Yasunori
Digital twin
Machine learning
Physics Informed Neural Networks
15:2015:40DC320Growth interface shape optimization and adaptive process control for InGaSb crystal growth under microgravity using machine learning
(Osaka U.) *(Stu)Ghritli Rachid, (JAXA-SOKENDAI) Inatomi Yuko, (Osaka U.) (Reg)Okano Yasunori
Machine Learning
Reinforcement Learning
Crystal Growth
(15:40–17:00) (Chair: Kim Sanghong)
15:4016:00DC321Multimodal Artificial Intelligence for Data-driven Developments of Complex Composite Materials
(AIST) *(Reg)Muroga Shun, Miki Yasuaki, (ADMAT) Honda Takashi, (AIST) Morita Hiroshi, Okazaki Toshiya, Hata Kenji
Multimodal AI
Materials Informatics
Composite Material
16:0016:20DC322Effect of physics-based feature engineering in predicting product yields of catalytic cracking reactions
(Shinshu U.) *(Stu)Yasuike Shun, (Reg)Osada Mitsumasa, (Reg)Shimada Iori
catalytic cracking
machine learning
feature engineering
16:2016:40DC323Developing identifiers to link materials databases
(UTokyo) *(Reg)Muraoka Koki, Munekata Tsubasa, Nakayama Akira
materials informatics
16:4017:00DC324Discusstion on initial sample selection for Bayesian optimization of compound combinations
(Meiji U.) *(Stu)Morishita Toshiharu, (Reg)Kaneko Hiromasa
Bayesian optimization
Machine learning

Technical program
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SCEJ 53rd Autumn Meeting (Nagano, 2022)

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