
SCEJ 57th Autumn Meeting
Sep. 14 (Mon) - 16 (Wed), 2026
Higashi-hiroshima Campus, Hiroshima University
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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: 2026-06-07 07:33:01
The keywords that frequently used in this topics code. | Keywords | Number | |
|---|---|---|---|
| artificial bone | 1 | ||
| in vivo | 1 | ||
| in vitro | 1 | ||
| Chemoinformatics | 1 | ||
| Solubility prediction | 1 | ||
| Machine learning | 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 |
Organizing Committee of SCEJ 57th Annual Meeting (2026)
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