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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 | 3 |  | |
| deep learning | 2 | | |
| phase equilibrium | 2 | | |
| viscosity | 2 | | |
| limonene | 1 | | |
| ACKN No. | Title/Author(s) | Keywords | Style |
|---|---|---|---|
| 31 | A Hybrid Prediction Model Combining GCN and PC-SAFT Equation of State with Molecular Shape | Perturbed chain-statistical associating fluid theory Equation of state Graph convolutional network | P |
| 87 | Elucidation of gas absorption characteristics of hydrate formed from hydrogel for gas storage process | Hydrate Hydrogel Gas Storage | P |
| 101 | Thermal storage properties of semiclathrate hydrates formed with N4446 propionate | semiclathrate melting temperature latent heat | P |
| 104 | Evaluation of Excess Surface Tensions at High Pressure Using Wilson-SurTen Model | excess surface tension high pressure ASOG-SurTen model | O |
| 312 | Prediction of Interaction Parameters for PR-vdW Model via PCM | Peng-Robinson equation of state Artificial neural network Polarizable continuum model | P |
| 313 | Search of terpene-derived deep eutectic solvents based on solid-liquid equilibrium measurements and predictions | Deep eutectic solvent Terpene Solid-liquid equilibria | O |
| 322 | Development of a Gradient-Constrained Machine Learning Model for Thermophysical Property Estimation of Ternary Mixtures | Ternary mixture Gradient constraint Machine learning | P |
| 380 | Development of vapor-liquid equilibrium prediction model using Raman spectrum informatics | raman spectrum vapor-liquid equilibrium activity coefficient | P |
| 384 | A Systematic Deep Learning Framework for Phase Equilibria: Evaluating the Synergy of Physically Informed and Structural Descriptors for Highly Accurate Prediction | Phase Equilibrium Prediction Sigma Profiles Deep Learning | P |
| 423 | Prediction of physical properties using deep learning | physical properties prediction deep learning | O |
| 491 | Crystal structure and phase equilibrium conditions of semiclathrate hydrates formed with C2 symmetry-alkylammonium salt | Semiclathrate hydrates Crystal structure Phase equilibrium | P |
| 567 | Modelling Phase Equilibrium of Phase-Separation Solvents for CO2 Capture Using COSMO-SAC with Extended Pitzer-Debye-Hückel Model | phase-separation solvent COSMO-SAC phase equilibrium | O |
| 597 | Measurement and Prediction of the Densities of CO2-Expanded Ethyl Lactate | density measurement CO2-expanded ethyl lactate | O |
| 619 | Viscosity Measurement of CO2/Organic Solvent/Polymer Ternary Systems: Extension to High-CO2 Concentration Regions | Viscosity CO2 expanded liquid CO2 painting | P |
| 632 | Molecular Dynamics Simulations of the Stability of Intercalation Compounds in Water | Intercalaton Molecular dynamics simulation Umbrella sampling | P |
| 638 | Measurement and correlation of the fluid mixture viscosities of carbon dioxide + 1-alkanols using a packed-bed under high pressure | carbon dioxide viscosity fluid mixture | P |
| 678 | Thermophysical Property Estimation of Multicomponent Mixtures Using Mixture Descriptors and Machine Learning | Multicomponent mixture Molecular descriptor Machine learning | P |
| 692 | Measurement and Correlation of the Diffusion Coefficients of Limonene in Pressurized Fluids | diffusion coefficient limonene pressurized fluid | P |
| 740 | Assessment of PR+COSMOSAC EOS for Machine Learning Based Prediction of Thermodynamic Properties | PR+COSMOSAC thermodynamic properties machine learning | O |
Organizing Committee of SCEJ 91st Annual Meeting (2026)
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