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List of received applications (By symposium/topics code)

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1) Fundamental properties

1-a. Equilibrium, Transport properties

Most recent update: 2025-12-31 22:44:01

The keywords that frequently used
in this topics code.		KeywordsNumber
machine learning3*
viscosity2
phase equilibrium2
deep learning2
Hydrate1

ACKN
No.		Title/Author(s)KeywordsStyle
31A Hybrid Prediction Model Combining GCN and PC-SAFT Equation of State with Molecular Shape
(Tokyo U. Sci.) *(Stu)Koizumi Katsuyuki, (Reg)Matsukawa Hiroaki, (Reg)Kazama Ryotaro, (Reg)Shono Atsushi, (Reg)Otake Katsuto		Perturbed chain-statistical associating fluid theory
Equation of state
Graph convolutional network
P
87Elucidation of gas absorption characteristics of hydrate formed from hydrogel for gas storage process
(YNU) *(Stu)Yanagi Haruna, (Reg)Muromachi Sanehiro		Hydrate
Hydrogel
Gas Storage
P
101Thermal storage properties of semiclathrate hydrates formed with N4446 propionate
(YNU) *(Stu)Tanaka Rio, (Stu)Nakamura Yuta, (Reg)Izato Yu-ichiro, (AIST) Takeya Satoshi, Suzuki Kiyofumi, Tenma Norio, (YNU) (Reg)Muromachi Sanehiro		semiclathrate
melting temperature
latent heat
P
104Evaluation of Excess Surface Tensions at High Pressure Using Wilson-SurTen Model
(Nihon U.) *(Reg)Tochigi Katsumi, (Reg)Matsuda Hiroyuki, (U. T. Malaysia) (Reg)Tsuji Tomoya, (Nihon U.) (Reg)Kurihara Kiyofumi		excess surface tension
high pressure
ASOG-SurTen model
O
312Prediction of Interaction Parameters for PR-vdW Model via PCM
(Tokyo U. Sci.) *(Stu)Sakamura Hikaru, (Reg)Matsukawa Hiroaki, (Reg)Kazama Ryotaro, (Reg)Shono Atsushi, (Reg)Otake Katsuto		Peng-Robinson equation of state
Artificial neural network
Polarizable continuum model
P
313Search of terpene-derived deep eutectic solvents based on solid-liquid equilibrium measurements and predictions
(Nihon U.) *(Reg)Matsuda Hiroyuki, (Stu)Hirano Ayato, (Universiti Teknologi Malaysia) (Reg)Tsuji Tomoya, (Nihon U.) (Reg)Kurihara Kiyofumi, (Reg)Tochigi Katsumi		Deep eutectic solvent
Terpene
Solid-liquid equilibria
O
322Development of a Gradient-Constrained Machine Learning Model for Thermophysical Property Estimation of Ternary Mixtures
(Shizuoka U.) *(Stu)Hatoyama Atsushi, (Reg)Murakami Yuya		Ternary mixture
Gradient constraint
Machine learning
P
380Development of vapor-liquid equilibrium prediction model using Raman spectrum informatics
(Science Tokyo) *(Stu)Matsuoka Yuki, (Reg)Orita Yasuhiko, (Reg)Shimoyama Yusuke		raman spectrum
vapor-liquid equilibrium
activity coefficient
P
384A Systematic Deep Learning Framework for Phase Equilibria: Evaluating the Synergy of Physically Informed and Structural Descriptors for Highly Accurate Prediction
(Nat. Taipei U. Tech.) *Hung Hsiu-Min, Hung Ying-Chieh		Phase Equilibrium Prediction
Sigma Profiles
Deep Learning
P
423Prediction of physical properties using deep learning
(TUS) (Reg)Ohe Shuzo		physical properties
prediction
deep learning
O
491Crystal structure and phase equilibrium conditions of semiclathrate hydrates formed with C2 symmetry-alkylammonium salt
(YNU) *(Stu)Asano Ayumu, (Reg)Muromachi Sanehiro		Semiclathrate hydrates
Crystal structure
Phase equilibrium
P
567Modelling Phase Equilibrium of Phase-Separation Solvents for CO2 Capture Using COSMO-SAC with Extended Pitzer-Debye-Hückel Model
(Nat. Taiwan U.) *Shih Yen-Jen, Lin Shiang-Tai, (Science Tokyo) (Reg)Shimoyama Yusuke		phase-separation solvent
COSMO-SAC
phase equilibrium
O
597Measurement and Prediction of the Densities of CO2-Expanded Ethyl Lactate
(Shizuoka U.) *(Stu)Wang Lida, Matsui Hibiki, Ichikawa Nono, (Chuo U.) (Reg)Onodera Norihiro, (Reg)Funazukuri Toshitaka, (Shizuoka U.) (Reg)Kong Chang Yi		density
measurement
CO2-expanded ethyl lactate
O
619Viscosity Measurement of CO2/Organic Solvent/Polymer Ternary Systems: Extension to High-CO2 Concentration Regions
(Tohoku Inst. Tech.) *(Stu)Kato Kenji, (Reg)Sato Yoshiyuki		Viscosity
CO2 expanded liquid
CO2 painting
P
632Molecular Dynamics Simulations of the Stability of Intercalation Compounds in Water
(Nihon U.) *(Reg)Okada Masaki, Sango Hiroyuki		Intercalaton
Molecular dynamics simulation
Umbrella sampling
P
638Measurement and correlation of the fluid mixture viscosities of carbon dioxide + 1-alkanols using a packed-bed under high pressure
(Chuo U.) *(Reg)Onodera Norihiro, (Reg)Funazukuri Toshitaka		carbon dioxide
viscosity
fluid mixture
P
678Thermophysical Property Estimation of Multicomponent Mixtures Using Mixture Descriptors and Machine Learning
(Shizuoka U.) *(Stu)Ikeda Keisuke, (Stu)Hatoyama Atsushi, (Reg)Murakami Yuya		Multicomponent mixture
Molecular descriptor
Machine learning
P
692Measurement and Correlation of the Diffusion Coefficients of Limonene in Pressurized Fluids
(Shizuoka U.) *(Stu)Ichikawa Nono, Fu Yao, (Stu)Wang Lida, (Chuo U.) (Reg)Funazukuri Toshitaka, (Shizuoka U.) (Reg)Kong Changyi		diffusion coefficient
limonene
pressurized fluid
P
740Assessment of PR+COSMOSAC EOS for Machine Learning Based Prediction of Thermodynamic Properties
(NTU) *(Int)Liang Hsinghao, Lin Shiangtai		PR+COSMOSAC
thermodynamic properties
machine learning
O
List of received applications (By topics code)
List of received applications
SCEJ 91st Annual Meeting (Kyoto, 2026)

Organizing Committee of SCEJ 91st Annual Meeting (2026)
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