| 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
| 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
| 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
| Protein Engineering
Antibody
Machine learning
| 7-a | 660 |
|---|
Day 1
13:40– 14:00 | I115 | Machine learning to develop extraction solvent for Ga(III) and separation of Ga(III) and In(III) by multi-stage solvation extraction
| Solvation Extraction
Gallium
Indium
| 4-f | 325 |
|---|
Day 2
9:20– 11:20 | PB270 | Design of a CO2 Adsorption Separation System Using a Machine Learning-Based Process Flowsheet
| 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
| 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
| Photocatalyst
Machine Learning
Kinetic Study
| 6-b | 423 |
|---|
Day 2
13:20– 15:20 | PC209 | Prediction of Ionic Conductivity in Solid Electrolytes Using a Machine Learning Model
| 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
| Machine Learning
Polymer
Permeability
| 6-g | 349 |
|---|
Day 2
13:20– 15:20 | PC220 | Development of machine learning model-based scores to evaluate pesticide-likeness
| Machine Learning
Quantitative Structure-Activity Relationship
Pesticide
| 6-g | 120 |
|---|
Day 2
13:20– 15:20 | PC222 | Optimization of Kalina Cycle with Snow and Ice Heat Source Appliyng Machine Learning
| Renewable Energy
Machine Learning
Thermal Energy
| 6-b | 429 |
|---|
Day 2
13:20– 15:20 | PC229 | Development of machine learning models for predicting the degradation activity and thermostability of plastic-degrading enzymes
| 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
| 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
| Parameter identification
Machine learning
Methanation
| 6-f | 449 |
|---|
Day 2
15:20– 15:40 | L220 | Analysis of protein adsorption onto hydrophilic brushes using machine learning
| antifouling
polymer brush
protein adsorption
| 12-a | 668 |
|---|
Day 2
15:40– 16:00 | G221 | Machine Learning-Driven Multiphysics-Scale Simulation of Spray Wet Etching
| Wet Etching
Simulation
Machine Learning
| 2-e | 46 |
|---|
Day 3
9:20– 11:20 | PD359 | Machine learning-driven optimization in flow reactions using Pd-immobilized catalysts
| Polymer immobilized Pd catalyst
Machine learning
Process optimization
| 5-f | 50 |
|---|
Day 3
9:20– 11:20 | PD368 | Continuous Production/Evaluation of Lipid nanoparticles by Microfluidic Devices and Modeling of Particle Size Prediction via Machine Learning
| Lipid nanoparticles
Microfluidics
Continuous process
| 5-f | 342 |
|---|
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