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SCEJ 91st Annual Meeting
Mar. 17 (Tue) - 19 (Thu), 2026
Yoshida Campus, Kyoto University
Japanese page
Most recent update: 2025-12-31 20:44:01
 The keywords that frequently used in this topics code. | Keywords | Number | |
|---|---|---|---|
| Machine Learning | 11 |  | |
| Ames Test | 2 | | |
| MRI analysis | 1 | | |
| ACKN No. | Title/Author(s) | Keywords | Style |
|---|---|---|---|
| 8 | Proposal of Crystal Structures for Novel Solid Electrolytes with High Ionic Conductivity Using Machine Learning | Solid Electrolyte Machine Learning Materials Informatics | P |
| 10 | Investigation of Highly-Accurate Variational Autoencoders for Multicomponent Crystal Structures | Machine Learning Crystal Structure VAE Latent Representation of Materials | P |
| 23 | Improving the predictive accuracy of drug-drug interaction prediction models using unlabeled, ingredient, and clinical data | Drug-drug interaction Machine learning Positive-unlabeled learning | P |
| 52 | Suggesting new drug candidates for schizophrenia using machine learning models and generative adversarial network | Machine learning Drug design Schizophrenia | P |
| 120 | Prediction of dielectric constant using calculated infrared spectra | infrared spectra machine learning | P |
| 133 | Investigation of the correlation between in vivo and in vitro experiments in bioceramics | artificial bone in vivo in vitro | P |
| 139 | Molecular generation to lower reorganization energy of organic semiconductors with an emphasis on the reliability of predictions of machine learning model | Machine learning Generative adversarial networks Organic semiconductor | P |
| 155 | Molecular Generation with Desired Number of Rings by Applying Conditional Hierarchical Variational Autoencoder to Explore Molecules Exhibiting Low Reorganization Energy | Conditional Hierarchical Variational Autoencoder Molecule Generation Reorganization Energy | P |
| 165 | A zero-shot quality prediction method for new combinations of materials and processes | Transfer learning Zero-shot regression Process Change | O |
| 175 | A Comparative Study of Molecular Descriptors to Improve Machine Learning Prediction of Ames Test Result | Ames test Machine learning | P |
| 315 | Multiscale regression of Alzheimer's disease progression indicators based on MRI images | Alzheimer's disease MRI analysis Deep learning | P |
| 316 | Fundamental study on disease prediction using brain MRI images by meta-learning | Machine Learning Neurodegenerative diseases Meta-learning | P |
| 336 | Combining Universal Machine Learning Interatomic Potential with Rare Event Sampling Methods for Polymer Polymerization and Degradation | uMLIP reaction rare event | O |
| 387 | Development of a machine learning model for predicting water vapor sorption of polysaccharides | Machine learning Polysaccharides Water vapor sorption | P |
| 538 | Design of novel plastic-degrading enzymes with high thermostability and degradation activity using machine learning models | machine learning bioinformatics plastics-degrading enzymes | P |
| 585 | Prediction of solubility in CO2 using COSMO-vacancy model | COSMO-vacancy activity coefficient supercritical CO2 | P |
| 614 | Development of a GCN prediction model for the Ames test using improved molecular expression methods | Machine Learning Ames Test Molecule Graph | P |
| 663 | Accelerating Automated Physical Model Building by Partitioning Equation-Variable Graphs | Automated physical modeling Equation-based modeling Graph partitioning | O |
Organizing Committee of SCEJ 91st Annual Meeting (2026)
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