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SCEJ 57th Autumn Meeting
Sep. 14 (Mon) - 16 (Wed), 2026
Higashi-hiroshima Campus, Hiroshima University
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: 2026-06-27 21:03:02
 The keywords that frequently used in this topics code. | Keywords | Number | |
|---|---|---|---|
| Machine learning | 6 |  | |
| crystal growth | 2 | | |
| RNA | 1 | | |
| ACKN No. | Title/Author(s) | Keywords | Style |
|---|---|---|---|
| 104 | Development of a Machine Learning Model for Predicting Solubility in Binary Solvent Systems at Arbitrary Temperatures | Chemoinformatics Solubility prediction Machine learning | O |
| 112 | Construction of machine learning models for predicting in vivo responses of artificial bone materials using in vitro indicators | artificial bone in vivo in vitro | O |
| 264 | Machine learning-based extrapolative prediction of dual-functional materials for CO and CH4 production via CO2 capture and reduction | CO2 capture and reduction (CCR) dual-functional materials (DFMs) machine learning (ML) | O |
| 301 | Optimal Design of e-Methanol Production Process Using Machine Learning and Superstructure Optimization | e-methanol Machine learning Process design | O |
| 305 | Construction of a miRNA-mRNA Binding Prediction Model Using Unknown Interaction Data and Performance Evaluation with Non-binding Data | Machine Learning bioinformatics RNA | O |
| 326 | Integrating DFT Reaction Kinetics with Group Contribution-Based Environmental Impacts for Sustainable Solvent Selection | Density functional theory (DFT) Solvents screening Reaction rates | O |
| 389 | The relationship between entropy production governing crystal growth and information entropy identifying pattern formation | entropy production information entropy crystal growth | O |
| 450 | Automated MOF Synthesis Using a Robotic Arm and an Electric Pipette | Metal-organic framework Laboratory automation Particle size control | O |
| 462 | Quantifying propagation distance of structural information in crystals | zeolite intergrowths crystal growth simulation | O |
| 474 | Fast estimation of H+ conductive barriers in perovskite oxides using the bond valence approach with machine learning | H+ conductive oxides Machine learning Solid oxide fuel cell | O |
| 481 | Verification and Evaluation of Large Language Models for Supporting Chemical Process Design | Large Language Models Chemical Process Benchmark | O |
| 578 | Development of an instability analysis for freezing process changes in cell-based products | Instability analysis Freezing process change Cell-based product | O |
| 635 | Advancing Chemical Engineering Research through an Integrated AI Platform | AI Cheminformatics Optimization | O |
| 754 | Development and evaluation using novel chemical feature-augmented GNN | machine learning molecular graph coarse graph | O |
| 778 | A data-driven framework for identifying critical solvent parameters in hydrogenation reactions | Machine learning Hydrogenation Solvent effect | O |
| 791 | Sequential Processing of Long Texts Using Large Language Models for Literature Mining of Endothelial Cell Culture Protocols | LLM Text mining Endothelial cell | O |
| 850 | Data-Driven Research in MMA Production Catalyst Development: Utilization of HTS Data Based on Reaction Engineering | high throughput kinetics catalyst deactivation | O |
| 909 | Development of a data-driven model for estimation of reaction diffusion mechanisms in porous solid catalysts | Reaction mechanism Reaction-diffusion system Data-driven | O |
| 920 | [Invited lecture] Hybrid modeling for connecting experimental data with process design | hybrid modeling pharmaceutical manufacturing design space | O |
| 921 | [Invited lecture] | TBD | O |
Organizing Committee of SCEJ 57th Annual Meeting (2026)
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