Last modified: 2023-05-16 07:24:56
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: | |||||
DC301 | [Invited lecture] Data-driven AI Laboratory and Cyber Catalysis | Data-driven Cyber Catalysis Computational Chemistry | ST-21 | 102 | |
DC303 | [Invited lecture] AI-Driven peptide/antibody molecule design for drug discovery | Artificial Intelligence Drug Discovery Antibody | ST-21 | 316 | |
(10:20–12:00) (Chair: | |||||
DC305 | Data-driven analysis of charge variants in monoclonal antibody production | Charge variant Monoclonal antibody PLS | ST-21 | 641 | |
DC306 | Multi-step approach for data-driven equipment condition assessment in biopharmaceutical drug product manufacturing | Predictive maintenance Unsupervised learning Industrial application | ST-21 | 590 | |
DC307 | Reinforcement learning to optimally control the bio and chemical processes | Reinforcement Learning Process control Optimal control | ST-21 | 60 | |
DC308 | Soft sensor study in film manufacturing process | Soft sensor Fault detection Film manufacturing process | ST-21 | 366 | |
DC309 | Prediction of phase equilibrium of water-organic compounds system at high-temperature and high-pressure using machine learning | machine learning prediction of phase equilibrium high-temperature and high-pressure | ST-21 | 532 | |
(13:00–14:20) (Chair: | |||||
DC313 | [Invited lecture] Exploration of functional inorganic thin-film materials using autonomous systems | autonomous synthesis inorganic materials functional thin films | ST-21 | 103 | |
DC315 | [Invited lecture] Data-driven polymer material development powered by Polymer SmartLab and Material DX | smart lab material DX database | ST-21 | 129 | |
(14:20–15:40) (Chair: | |||||
DC317 | Inverse design of polymer membrane structure for gas separation using Junction Tree VAE machine learning | machine learning polymer membrane gas separation | ST-21 | 712 | |
DC318 | Design of both membrane-based process and membrane materials with machine learning | Membrane module Materials Informatics Process design | ST-21 | 187 | |
DC319 | [Featured presentation] Development of digital twin of the bulk single crystal growth of Si by using PINNs (Physics Informed Neural Networks) | Digital twin Machine learning Physics Informed Neural Networks | ST-21 | 396 | |
DC320 | Growth interface shape optimization and adaptive process control for InGaSb crystal growth under microgravity using machine learning | Machine Learning Reinforcement Learning Crystal Growth | ST-21 | 428 | |
(15:40–17:00) (Chair: | |||||
DC321 | Multimodal Artificial Intelligence for Data-driven Developments of Complex Composite Materials | Multimodal AI Materials Informatics Composite Material | ST-21 | 669 | |
DC322 | Effect of physics-based feature engineering in predicting product yields of catalytic cracking reactions | catalytic cracking machine learning feature engineering | ST-21 | 101 | |
DC323 | Developing identifiers to link materials databases | materials informatics database | ST-21 | 580 | |
DC324 | Discusstion on initial sample selection for Bayesian optimization of compound combinations | Bayesian optimization Machine learning Clustering | ST-21 | 328 |
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SCEJ 53rd Autumn Meeting (Nagano, 2022)