machine learning - Program search result - SCEJ 90th Annual Meeting (Tokyo, 2025)

`SCEJ 90th Annual Meeting (Tokyo, 2025)`

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Program search result : machine learning : 28 programs

The preprints are now open. These can be viewed by clicking the Paper IDs.
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All other sessions will be the on-site only sessions.

Keywords field exact matches “machine learning”; 28 programs are found.
The search results are sorted by the start time.

Time	Paper ID	Title / Authors	Keywords	Topic code	Ack. number
Day 1 13:15– 13:45	G114	[Divisional Award] Frontier Prize lecture: Surrogate-Modeling of Powder Flow and Mixing using Machine Learning (Osaka Metro. U.) *(Reg)Nakamura Hideya, (Stu)Kishida Naoki, (Reg)Ohsaki Shuji, (Reg)Watano Satoru	Powder mixing Discrete element method Machine learning	X-51	448
Day 1 13:20– 15:20	PA102	Machine-learning-guided directed evolution of antibody fragments: simultaneous optimization of affinity, expression, and thermal stability (Tohoku U.) *(Reg)Ito Tomoyuki, Kawada Sakiya, (Reg)Nakazawa Hikaru, (Tokushima U.) Murakami Akikazu, (Tohoku U.) (Reg)Umetsu Mitsuo	VHH machine learning molecular evolution	7-a	457
Day 1 13:20– 15:20	PA146	Development of a direct machine learning process from cytotoxicity on the subject of a novel T-cell engaging antibody (Tohoku U.) *(Stu)Ito Kohei, (Reg)Ito Tomoyuki, Sasaki Renan, Tanno Ryo, (Reg)Nakazawa Hikaru, (Reg)Umetsu Mitsuo	Protein Engineering Antibody Machine learning	7-a	660
Day 1 13:20– 15:20	PA151	Data augmentation technology for morphology-based prediction model of human fibroblasts (Nagoya U.) *(Stu)Katayama S., Kimura K., (Reg)Tanaka K., (Reg)Kato R.	Data augmentation Cell morphology Machine learning	7-e	207
Day 2 9:20– 11:20	PB270	Design of a CO₂ Adsorption Separation System Using a Machine Learning-Based Process Flowsheet (Nagoya U.) *(Stu·PCEF)Fujii Yota, (Reg)Matsuda Keigo	CO2 Machine Learning Adsorption	4-e	618
Day 2 11:00– 11:20	K207	Rapid estimation of crystal structures of perovskite oxides using a soft bond valence method with machine learning (Science Tokyo) *(Reg)Kameda Keisuke, Ito Kazuma, (Reg)Ihara Manabu, (Reg)Manzhos Sergei	bond valence machine learning proton conductors	9-e	157
Day 2 13:20– 15:20	PC203	Modeling of Photocatalytic Degradation Kinetics of Azo Compounds Applying Machine Learning (NIT Nagaoka) *(Stu·PCEF)Nakano Haruka, Kasuga Syunsuke, (Reg)Atsumi Ryosuke	Photocatalyst Machine Learning Kinetic Study	6-b	423
Day 2 13:20– 15:20	PC205	Construction of property prediction model and inverse analysis of the model in carbon material manufacturing process with different batch times (Meiji U.) *(Stu)Matsubara Masayoshi, (Mitsubishi Chemical) (Cor)Sasaki Ryo, (Cor)Takahara Jun, (Cor)Moritake Shinji, (Cor)Harada Yasuyuki, (Meiji U.) (Reg)Kaneko Hiromasa	Machine learning Batch time	6-e	240
Day 2 13:20– 15:20	PC209	Prediction of Ionic Conductivity in Solid Electrolytes Using a Machine Learning Model (Meiji U.) *(Stu)Ishikawa Eri, (Reg)Kaneko Hiromasa	Solid Electrolyte Machine Learning Materials Informatics	6-f	244
Day 2 13:20– 15:20	PC213	Development of machine learning models to predict gas permeability from monomer structures and properties of polymer materials (Meiji U.) *(Stu)Ochiai Haruki, Nagai Kazukiyo, (Reg)Kaneko Hiromasa	Machine Learning Polymer Permeability	6-g	349
Day 2 13:20– 15:20	PC216	Design of new acetylcholinesterase inhibitors using a Generative Adversarial Network (Meiji U.) *(Stu)Ando Ruka, (Reg)Kaneko Hiromasa	acetylcholinesterase Generative Adversarial Network machine learning	6-g	43
Day 2 13:20– 15:20	PC218	Development and improvement of an odor prediction model based on molecular structure using olfactory receptor information (Meiji U.) *(Stu)Wakutsu Yuta, (Reg)Kaneko Hiromasa	Odor Protein Machine Learning	6-g	93
Day 2 13:20– 15:20	PC220	Development of machine learning model-based scores to evaluate pesticide-likeness (Meiji U.) *(Stu)Sakai Yuta, (Reg)Kaneko Hiromasa	Machine Learning Quantitative Structure-Activity Relationship Pesticide	6-g	120
Day 2 13:20– 15:20	PC221	Exploration of Candidate Molecules for Organic Semiconductor Materials Using Generative Models (Meiji U.) *(Stu)Nakanishi Yamato, (Panasonic Ind.) Matsuzawa Nobuyuki N, Maeshima Hiroyuki, Ando Tatsuhito, (Meiji U.) (Reg)Kaneko Hiromasa	Machine learning Organic semiconductor Hierarchical Variational Autoencoder	6-f	242
Day 2 13:20– 15:20	PC222	Optimization of Kalina Cycle with Snow and Ice Heat Source Appliyng Machine Learning (NIT Nagaoka) *(Stu·PCEF)Koyama Yamato, (Reg)Atsumi Ryosuke	Renewable Energy Machine Learning Thermal Energy	6-b	429
Day 2 13:20– 15:20	PC223	Development of a method for predicting drug-drug interactions considering negative data mixed with positive data (Meiji U.) *(Stu)Kosakai Soma, (Reg)Kaneko Hiromasa	Drug-drug interaction Machine learning Positive-unlabeled learning	6-f	119
Day 2 13:20– 15:20	PC226	Prediction of CO₂ solubility in deep eutectic solvents from machine learning models with molecular sigma-profiles (Nat. Central U.) *Chen Ying-Jung, Hsieh Chieh-Ming	deep eutectic solvents machine learning COSMO-SAC model	6-e	644
Day 2 13:20– 15:20	PC227	Pure Component Parameter Estimation for PC-SAFT EOS from Deep Neural Network with Molecular Sigma-Profiles (Nat. Central U.) *Chen Jia-Hao, Hsieh Chieh-Ming	PC-SAFT EOS Machine Learning Sigma Profile	6-e	661
Day 2 13:20– 15:20	PC229	Development of machine learning models for predicting the degradation activity and thermostability of plastic-degrading enzymes (Meiji U.) *(Stu)Ohkuma Ayami, (Reg)Kaneko Hiromasa	machine learning bioinformatics plastics-degrading enzymes	6-g	378
Day 2 13:20– 15:20	PC234	Building machine learning models to suggest new drug candidates for schizophrenia (Meiji U.) *(Stu)Kimura Shoei, (Reg)Kaneko Hiromasa	Machine learning Drug design Schizophrenia	6-g	373
Day 2 13:20– 15:20	PC235	Parameter identification for a chemical reaction rate by a machine learning approach (Mizuho R&T) *(Cor)Nakamura Kotaro, (Cor)Yamade Yoshinobu, (Cor)Mizuhara Shinichi, (Cor)Koizumi Hiroshi, Nagano Katsuhiro	Parameter identification Machine learning Methanation	6-f	449
Day 2 14:00– 14:30	E215	[Invited lecture] Computational approach to adsorption property and nanostructure in organically-modified 2D interlayer (Kogakuin U.) *(Reg)Miyagawa Masaya, (Reg)Takaba Hiromitsu	Machine learning Molecular dynamics Adsorption	K-5	622
Day 2 15:40– 16:00	G221	Machine Learning-Driven Multiphysics-Scale Simulation of Spray Wet Etching (Toppan HD) *(Reg)Yokota Ryohsuke, Oguchi Ayumi, Ota Shinji, (Reg)Wada Shinichi	Wet Etching Simulation Machine Learning	2-e	46
Day 3 9:20– 11:20	PD329	Proposal for a new framework to construct empirical rule (Osaka Pref. U.) *(Stu·PCEF)Nishimura Ritsuki, (Osaka Metro. U.) (Reg)Yamamoto Takuya	CFD simulation Machine learning	2-g	188
Day 3 9:20– 11:20	PD359	Machine learning-driven optimization in flow reactions using Pd-immobilized catalysts (Kyushu U.) *(Stu)Zhou Xincheng, (Reg·PCEF)Matsumoto Hikaru, (Reg)Nagao Masanori, (Reg)Miura Yoshiko	Polymer immobilized Pd catalyst Machine learning Process optimization	5-f	50
Day 3 9:40– 10:00	M303	AI-Driven 3D Bioprinting System for Optimizing Printing Performance (Osaka U.) *(Stu)Zhang Colin, (Stu)Elvitigala Kelum, (Reg)Sakai Shinji	3D bioprinting Process optimization Machine learning	7-f	248
Day 3 10:00– 10:20	H304	Development of uroflowmetry using urine jet images (Saitama U.) *(Reg)Homma Shunji, (Stu)Kaneko Riki, Kiyama Akihito, Kan Donghyuk, (Saitama Med. U.) Takeshita Hideki	uroflowmetry Machine learning CFD	6-f	27
Day 3 13:20– 13:40	G314	Development of a data-driven framework for optimal design of air filter microstructures (Hiroshima U.) *(Reg)Ishigami Toru, Tsuzuki Hina, Shirzadi Mohammadreza, (Reg)Fukasawa Tomonori, (Reg)Fukui Kunihiro	Air filter Numerical simulation Machine learning	2-a	300

Time

Paper
ID

Title / Authors

Keywords

Topic code

Ack.
number

Day 1
13:15– 13:45

G114

[Divisional Award] Frontier Prize lecture: Surrogate-Modeling of Powder Flow and Mixing using Machine Learning

(Osaka Metro. U.) *(Reg)Nakamura Hideya, (Stu)Kishida Naoki, (Reg)Ohsaki Shuji, (Reg)Watano Satoru

Powder mixing
Discrete element method
Machine learning

X-51

448

Day 1
13:20– 15:20

PA102

Machine-learning-guided directed evolution of antibody fragments: simultaneous optimization of affinity, expression, and thermal stability

(Tohoku U.) *(Reg)Ito Tomoyuki, Kawada Sakiya, (Reg)Nakazawa Hikaru, (Tokushima U.) Murakami Akikazu, (Tohoku U.) (Reg)Umetsu Mitsuo

VHH
machine learning
molecular evolution

7-a

457

Day 1
13:20– 15:20

PA146

Development of a direct machine learning process from cytotoxicity on the subject of a novel T-cell engaging antibody

(Tohoku U.) *(Stu)Ito Kohei, (Reg)Ito Tomoyuki, Sasaki Renan, Tanno Ryo, (Reg)Nakazawa Hikaru, (Reg)Umetsu Mitsuo

Protein Engineering
Antibody
Machine learning

7-a

660

Day 1
13:20– 15:20

PA151

Data augmentation technology for morphology-based prediction model of human fibroblasts

(Nagoya U.) *(Stu)Katayama S., Kimura K., (Reg)Tanaka K., (Reg)Kato R.

Data augmentation
Cell morphology
Machine learning

7-e

207

Day 2
9:20– 11:20

PB270

Design of a CO₂ Adsorption Separation System Using a Machine Learning-Based Process Flowsheet

(Nagoya U.) *(Stu·PCEF)Fujii Yota, (Reg)Matsuda Keigo

CO2
Machine Learning
Adsorption

4-e

618

Day 2
11:00– 11:20

K207

Rapid estimation of crystal structures of perovskite oxides using a soft bond valence method with machine learning

(Science Tokyo) *(Reg)Kameda Keisuke, Ito Kazuma, (Reg)Ihara Manabu, (Reg)Manzhos Sergei

bond valence
machine learning
proton conductors

9-e

157

Day 2
13:20– 15:20

PC203

Modeling of Photocatalytic Degradation Kinetics of Azo Compounds Applying Machine Learning

(NIT Nagaoka) *(Stu·PCEF)Nakano Haruka, Kasuga Syunsuke, (Reg)Atsumi Ryosuke

Photocatalyst
Machine Learning
Kinetic Study

6-b

423

Day 2
13:20– 15:20

PC205

Construction of property prediction model and inverse analysis of the model in carbon material manufacturing process with different batch times

(Meiji U.) *(Stu)Matsubara Masayoshi, (Mitsubishi Chemical) (Cor)Sasaki Ryo, (Cor)Takahara Jun, (Cor)Moritake Shinji, (Cor)Harada Yasuyuki, (Meiji U.) (Reg)Kaneko Hiromasa

Machine learning
Batch time

6-e

240

Day 2
13:20– 15:20

PC209

Prediction of Ionic Conductivity in Solid Electrolytes Using a Machine Learning Model

(Meiji U.) *(Stu)Ishikawa Eri, (Reg)Kaneko Hiromasa

Solid Electrolyte
Machine Learning
Materials Informatics

6-f

244

Day 2
13:20– 15:20

PC213

Development of machine learning models to predict gas permeability from monomer structures and properties of polymer materials

(Meiji U.) *(Stu)Ochiai Haruki, Nagai Kazukiyo, (Reg)Kaneko Hiromasa

Machine Learning
Polymer
Permeability

6-g

349

Day 2
13:20– 15:20

PC216

Design of new acetylcholinesterase inhibitors using a Generative Adversarial Network

(Meiji U.) *(Stu)Ando Ruka, (Reg)Kaneko Hiromasa

acetylcholinesterase
Generative Adversarial Network
machine learning

6-g

Day 2
13:20– 15:20

PC218

Development and improvement of an odor prediction model based on molecular structure using olfactory receptor information

(Meiji U.) *(Stu)Wakutsu Yuta, (Reg)Kaneko Hiromasa

Odor
Protein
Machine Learning

6-g

Day 2
13:20– 15:20

PC220

Development of machine learning model-based scores to evaluate pesticide-likeness

(Meiji U.) *(Stu)Sakai Yuta, (Reg)Kaneko Hiromasa

Machine Learning
Quantitative Structure-Activity Relationship
Pesticide

6-g

120

Day 2
13:20– 15:20

PC221

Exploration of Candidate Molecules for Organic Semiconductor Materials Using Generative Models

(Meiji U.) *(Stu)Nakanishi Yamato, (Panasonic Ind.) Matsuzawa Nobuyuki N, Maeshima Hiroyuki, Ando Tatsuhito, (Meiji U.) (Reg)Kaneko Hiromasa

Machine learning
Organic semiconductor
Hierarchical Variational Autoencoder

6-f

242

Day 2
13:20– 15:20

PC222

Optimization of Kalina Cycle with Snow and Ice Heat Source Appliyng Machine Learning

(NIT Nagaoka) *(Stu·PCEF)Koyama Yamato, (Reg)Atsumi Ryosuke

Renewable Energy
Machine Learning
Thermal Energy

6-b

429

Day 2
13:20– 15:20

PC223

Development of a method for predicting drug-drug interactions considering negative data mixed with positive data

(Meiji U.) *(Stu)Kosakai Soma, (Reg)Kaneko Hiromasa

Drug-drug interaction
Machine learning
Positive-unlabeled learning

6-f

119

Day 2
13:20– 15:20

PC226

Prediction of CO₂ solubility in deep eutectic solvents from machine learning models with molecular sigma-profiles

(Nat. Central U.) *Chen Ying-Jung, Hsieh Chieh-Ming

deep eutectic solvents
machine learning
COSMO-SAC model

6-e

644

Day 2
13:20– 15:20

PC227

Pure Component Parameter Estimation for PC-SAFT EOS from Deep Neural Network with Molecular Sigma-Profiles

(Nat. Central U.) *Chen Jia-Hao, Hsieh Chieh-Ming

PC-SAFT EOS
Machine Learning
Sigma Profile

6-e

661

Day 2
13:20– 15:20

PC229

Development of machine learning models for predicting the degradation activity and thermostability of plastic-degrading enzymes

(Meiji U.) *(Stu)Ohkuma Ayami, (Reg)Kaneko Hiromasa

machine learning
bioinformatics
plastics-degrading enzymes

6-g

378

Day 2
13:20– 15:20

PC234

Building machine learning models to suggest new drug candidates for schizophrenia

(Meiji U.) *(Stu)Kimura Shoei, (Reg)Kaneko Hiromasa

Machine learning
Drug design
Schizophrenia

6-g

373

Day 2
13:20– 15:20

PC235

Parameter identification for a chemical reaction rate by a machine learning approach

(Mizuho R&T) *(Cor)Nakamura Kotaro, (Cor)Yamade Yoshinobu, (Cor)Mizuhara Shinichi, (Cor)Koizumi Hiroshi, Nagano Katsuhiro

Parameter identification
Machine learning
Methanation

6-f

449

Day 2
14:00– 14:30

E215

[Invited lecture] Computational approach to adsorption property and nanostructure in organically-modified 2D interlayer

(Kogakuin U.) *(Reg)Miyagawa Masaya, (Reg)Takaba Hiromitsu

Machine learning
Molecular dynamics
Adsorption

K-5

622

Day 2
15:40– 16:00

G221

Machine Learning-Driven Multiphysics-Scale Simulation of Spray Wet Etching

(Toppan HD) *(Reg)Yokota Ryohsuke, Oguchi Ayumi, Ota Shinji, (Reg)Wada Shinichi

Wet Etching
Simulation
Machine Learning

2-e

Day 3
9:20– 11:20

PD329

Proposal for a new framework to construct empirical rule

(Osaka Pref. U.) *(Stu·PCEF)Nishimura Ritsuki, (Osaka Metro. U.) (Reg)Yamamoto Takuya

CFD simulation
Machine learning

2-g

188

Day 3
9:20– 11:20

PD359

Machine learning-driven optimization in flow reactions using Pd-immobilized catalysts

(Kyushu U.) *(Stu)Zhou Xincheng, (Reg·PCEF)Matsumoto Hikaru, (Reg)Nagao Masanori, (Reg)Miura Yoshiko

Polymer immobilized Pd catalyst
Machine learning
Process optimization

5-f

Day 3
9:40– 10:00

M303

AI-Driven 3D Bioprinting System for Optimizing Printing Performance

(Osaka U.) *(Stu)Zhang Colin, (Stu)Elvitigala Kelum, (Reg)Sakai Shinji

3D bioprinting
Process optimization
Machine learning

7-f

248

Day 3
10:00– 10:20

H304

Development of uroflowmetry using urine jet images

(Saitama U.) *(Reg)Homma Shunji, (Stu)Kaneko Riki, Kiyama Akihito, Kan Donghyuk, (Saitama Med. U.) Takeshita Hideki

uroflowmetry
Machine learning
CFD

6-f

Day 3
13:20– 13:40

G314

Development of a data-driven framework for optimal design of air filter microstructures

(Hiroshima U.) *(Reg)Ishigami Toru, Tsuzuki Hina, Shirzadi Mohammadreza, (Reg)Fukasawa Tomonori, (Reg)Fukui Kunihiro

Air filter
Numerical simulation
Machine learning

2-a

300

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