Hall DC, Day 3 - Hall and day program - SCEJ 53rd Autumn Meeting (Nagano, 2022)

`SCEJ 53rd Autumn Meeting (Nagano, 2022)`

Last modified: 2023-05-16 07:24:56

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Hall and day program : Hall DC, Day 3

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Hall DC(C3 1F 102), Day 3(Sep. 16)

ST-21

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
ST-21 [Trans-Division Symposium] Frontiers of Data-driven Research and Development
(9:00–10:20) (Chair: Mukaida Shiho)
9:00– 9:40	DC301	[Invited lecture] Data-driven AI Laboratory and Cyber Catalysis (Shinshu U.) (Reg)Koyama Michihisa	Data-driven Cyber Catalysis Computational Chemistry	ST-21	102
9:40– 10:20	DC303	[Invited lecture] AI-Driven peptide/antibody molecule design for drug discovery (MOLCURE) Tamaki Satoshi	Artificial Intelligence Drug Discovery Antibody	ST-21	316
(10:20–12:00) (Chair: Toya Yoshihiro)
10:20– 10:40	DC305	Data-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 PLS	ST-21	641
10:40– 11:00	DC306	Multi-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	ST-21	590
11:00– 11:20	DC307	Reinforcement learning to optimally control the bio and chemical processes (Kyoto U.) (Int)Oh Tae Hoon	Reinforcement Learning Process control Optimal control	ST-21	60
11:20– 11:40	DC308	Soft 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	ST-21	366
11:40– 12:00	DC309	Prediction 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	ST-21	532
(13:00–14:20) (Chair: Kaneko Shogo)
13:00– 13:40	DC313	[Invited lecture] Exploration of functional inorganic thin-film materials using autonomous systems (Tokyo Tech) Shimizu Ryota	autonomous synthesis inorganic materials functional thin films	ST-21	103
13:40– 14:20	DC315	[Invited lecture] Data-driven polymer material development powered by Polymer SmartLab and Material DX (NIMS) Naito Masanobu	smart lab material DX database	ST-21	129
(14:20–15:40) (Chair: Shimada Iori)
14:20– 14:40	DC317	Inverse 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	ST-21	712
14:40– 15:00	DC318	Design 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	ST-21	187
15:00– 15:20	DC319	[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	ST-21	396
15:20– 15:40	DC320	Growth 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	ST-21	428
(15:40–17:00) (Chair: Kim Sanghong)
15:40– 16:00	DC321	Multimodal 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	ST-21	669
16:00– 16:20	DC322	Effect 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	ST-21	101
16:20– 16:40	DC323	Developing identifiers to link materials databases (UTokyo) *(Reg)Muraoka Koki, Munekata Tsubasa, Nakayama Akira	materials informatics database	ST-21	580
16:40– 17:00	DC324	Discusstion on initial sample selection for Bayesian optimization of compound combinations (Meiji U.) *(Stu)Morishita Toshiharu, (Reg)Kaneko Hiromasa	Bayesian optimization Machine learning Clustering	ST-21	328

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SCEJ 53rd Autumn Meeting (Nagano, 2022)

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