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SCEJ 56th Autumn Meeting
Sep. 16 (Tue) - 18 (Thu), 2025
Toyosu Campus, Shibaura Institute of Technology
Japanese page
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.
Most recent update: 2025-12-05 14:48:01
 The keywords that frequently used in this topics code. | Keywords | Number | |
|---|---|---|---|
| Machine learning | 7 |  | |
| Computational flow dynamics | 1 | | |
| ACKN No. | Title/Author(s) | Keywords | Style |
|---|---|---|---|
| 31 | Machine Learning-Aided Prediction of the Lower Critical Solution Temperature of Thermoresponsive Copolymers | Thermoresponsive polymer Lower critical solution temperature Machine learning | O |
| 97 | Prediction of electrostatic inkjet printing characteristics using CNN with electric field distribution as input | Electrostatic inkjet Convolutional Neural Networks Electric field distribution | O |
| 171 | High-resolution flow analysis in cell culture tanks using a combination of PINNs and CFD | Suspension culture Computational flow dynamics Neural Networks | O |
| 178 | Development of a method for predicting drug-drug interactions using food ingredients as substitute for negative data | Drug-drug interaction Machine learning Positive-unlabeled learning | O |
| 235 | Prediction of ultrafiltration membrane fouling and optimization of operating conditions using machine learning models | Machine Learning Ultrafiltration Membrane Fouling | O |
| 346 | Development of automated and autonomous experimental systems for electrode slurry: challenges and approaches | automatic and autonomous laboratory slurry viscosity | O |
| 469 | Machine Learning for Flow Suzuki Coupling Reactions Using Immobilized Pd Catalysts | Polymer immobilized Pd catalyst Machine learning Process optimization | O |
| 471 | A Heterogeneous Transfer Learning Approach for Manufacturing Method Transitions in Pharmaceutical Processes | Heterogeneous Transfer Learning Manufacturing Method Transition Pharmaceutical Manufacturing | O |
| 744 | Novel feature suggestion for Predictive Model of the Bacterial Reverse Mutation Test | Machine Learning Variational Autoencoder COSMO method | O |
| 746 | Applications of transfer learning in catalyst and polymer development | transfer learning machine learning data driven approach | O |
| 763 | Development of uncertainty quantification methods using sequential Monte Carlo methods and demonstration in reactor modeling | Sequential Monte Carlo Uncertainty quantification Methanation | O |
| 923 | Improvement of reaction mechanism prediction by chemical reaction neural network and its extension to heterogeneous catalytic reaction systems | chemical reaction neural network kinetic model heterogeneous catalyst | O |
| 935 | [Invited lecture] Accelerating Catalyst Development through High-Throughput Experiments | Catalyst development High-throughput screening Autonomous experimentation | O |
| 936 | High-Accuracy Building Electricity Demand Forecasting Using Group Encoding for High-Dimensional Binary Data | group encoding demand forecasting big data | O |
| 938 | [Invited lecture] Machine learning-enabled spectroscopy and materials discovery | Machine learning XANES/ELNES Materials discovery | O |
| 939 | [Invited lecture] Practical studies of data-driven materials research based on accelerating the data cycle | autonomous experiment machine learning potential spectral analysis | O |
| 945 | [Invited lecture] Perspectives and Challenges in Laboratory Automation Supporting Data-Driven Science in the field of Biotechnology | Laboratory automation Biotechnology Data-Driven Science | O |
| 946 | [Invited lecture] Optimization Applications in Business and Information Systems | Optimization Problems Combinatorial Optimization Business Applications | O |
Organizing Committee of SCEJ 56th Autumn Meeting (2025)
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