Last modified: 2024-09-19 02:24:28
Data science has been rapidly developing in recent years as the fourth science following experimental science, theoretical science, and computational science. In the field of chemical engineering, data-driven science, which derives superior materials and processes by making full use of a large amount of accumulated data and information, is becoming increasingly important, and many efforts are being made. This symposium will have speakers who are making pioneering efforts toward a data-driven society from various viewpoints and discuss future research and development.
Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
---|---|---|---|---|---|
Hall E, Day 2 | |||||
(13:00–14:20) (Chair: | |||||
E213 | Calculating Potential of Solar Cell on Building Façades for Japan's Energy System Optimization in 2050 | Solar cell facade installation Energy system optimization | ST-22 | 580 | |
E214 | Prediction model for real-time power generation of solar cells with shadow simulation based on module structure and irradiance fraction | Solar cell Energy system Prediction model | ST-22 | 865 | |
E215 | Analysis of anion selectivity in Mg-based Layered Double Hydroxide using a Universal Neural Network Potential | machine learning layered double hydroxide (LDH) anion selectivity | ST-22 | 732 | |
E216 | Molecular design using deep learning and vecror annealing | Deep learning Quantum anealing Generative model | ST-22 | 1099 | |
(14:40–16:00) (Chair: | |||||
E218 | [The Outstanding Paper Award] Optimization of metal nanoparticle synthesis conditions using automated flow system and machine learning | Nanoparticle Microreactor Machine Learning | ST-22 | 448 | |
E219 | Statistical consideration for investigating a direct correlation between solvent effect and hydrogenation of nitrobenzene using flow-tubular reactor | statistical analysis hydrogenation flow reactor | ST-22 | 121 | |
E220 | Development of a product composition prediction model for catalytic cracking reactions and its application to the prediction of reactions with unknown feedstocks | physics-informed machine learning catalytic cracking reaction prediction | ST-22 | 1128 | |
E221 | Automated reaction process analysis using data-driven approaches | Dynamic mode decomposition Chemical reaction Machine learning | ST-22 | 969 | |
Hall E, Day 3 | |||||
(9:00–10:20) (Chair: | |||||
E301 | Development of a transfer learning method using O-PLS | O-PLS transfer learning calibration transfer | ST-22 | 311 | |
E302 | Application of Transfer Learning Using Generative Adversarial Networks to Virtual Sensing | transfer learning soft-sensor Generative Adversarial Networks | ST-22 | 66 | |
E303 | Control of foam on white water and improvement of operational efficiency by modeling a soft sensor | soft sensor machine learning factory automation | ST-22 | 95 | |
E304 | 13C-MFA software with a support function for high accuracy flux estimations by proposing additional experiments | 13C-metabolic flux analysis flux analysis software | ST-22 | 615 | |
(10:40–12:00) (Chair: | |||||
E306 | [Invited lecture] Autonomous Materials Search | Machine learning Autonomous Active learning | ST-22 | 145 | |
E308 | [Invited lecture] Representation and Generation of Crystal Structures with Deep Learning | Materials informatics Deep learning Crystal structure | ST-22 | 894 | |
(13:00–13:40) (Chair: | |||||
E313 | [Invited lecture] Introduction to Case Studies on the Development and Utilization of Informatics Technologies | Informatics Industry R&D acceleration | ST-22 | 144 | |
(13:40–14:20) (Chair: | |||||
E315 | [Invited lecture] Digital infrastructure to reduce time to market / R&D to production | digital AI data integration | ST-22 | 616 | |
(14:20–15:00) (Chair: | |||||
E317 | [Invited lecture] Understanding the diversity of metabolisms with the advances of computational metabolomics | metabolomics mass spectrometry informatics | ST-22 | 142 |
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SCEJ 55th Autumn Meeting (Sapporo, 2024)