SCEJ 55th Autumn Meeting (Sapporo, 2024)
Program search result : Machine learning : 51 programs
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Keywords field exact matches “Machine learning”; 51 programs are found. (“Poster with Flash” presentations are double-counted.)
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Day 1 | M101 | Machine learning model for predicting three-dimensional flow field inside a face mask obtained from X-ray CT | face mask numerical simulation machine learning | SY-52 | 527 |
| Day 1 | YB128 | Construction of yield prediction model in catalytic cracking of vegetable oils considering fatty acid composition | catalytic cracking vegetable oil machine learning | SY-63 | 697 |
| Day 1 | YB130 | Construction of Prediction Model for the Yield of Suzuki-Miyaura Type Cross-Coupling Reactions Using Polymeric Ni Catalysts and Proposal of Novel Polymeric Ni Catalysts | Machine Learning Polymeric Ni Catalysts Chemical Reactions | SY-63 | 271 |
| Day 1 | YB132 | Application of Machine Learning in Designing the Width of Permeable Reactive Barriers Based on Reduction Mechanisms | Machine learning Permeable reactive barrier LUMO | SY-63 | 388 |
| Day 1 | YB158 | Energy-saving design of ethylbenzene production process using machine learning and Bayesian optimization | Machine learning Bayesian optimization Process design | SY-63 | 76 |
| Day 1 | M102 | Data analysis for unsteady flow under low oscillating Reynolds number conditions using machine learning | oscillatory flow machine learning PIV analysis | SY-52 | 618 |
| Day 1 | N102 | Rapid measurement of emulsion viscosity using machine learning | Emulsion Viscosity Machine Learning | SY-53 | 129 |
| Day 1 | T102 | A Knowledge-Based Approach for Structural Optimization of CO2 Separation Systems | CO2 Machine Learning Knowledge Base | ST-21 | 63 |
| Day 1 | M103 | Applications of Bayesian Machine Learning to Complex Flows | Machine Learning Polymer Melt | SY-52 | 1038 |
| Day 1 | M104 | Prediction of flow patterns in liquid-liquid two-phase flow within capillaries using machine learning | liquid-liquid flow slug flow machine learning | SY-52 | 1071 |
| Day 1 | YA189 | Investigation of the use of information from olfactory receptors for molecular odor prediction | Odor Protein Machine Learning | SY-68 | 165 |
| Day 1 | Q109 | Development of a graph neural network (GNN) model for predicting the solubility of organic compounds in supercritical CO2 | Supercritical Solubility prediction Machine learning | SY-73 | 851 |
| Day 1 | YA189 | Investigation of the use of information from olfactory receptors for molecular odor prediction | Odor Protein Machine Learning | SY-68 | 165 |
| Day 1 | L113 | Machine learning model estimating blood glucose monitoring from mid-infrared spectra measured by the photothermal monitoring method | mid-infrared spectra noninvasive blood glucose monitoring machine learning | SY-66 | 102 |
| Day 1 | Q113 | [Invited lecture] Physical property prediction based on machine learning and ab initio calculation | Machine learning Physical property Transfer learning | SY-73 | 537 |
| Day 1 | L115 | Development of a soft sensor model that takes into account the dynamic characteristics of the process and two similar qualities | machine learning soft sensor time delay | SY-66 | 260 |
| Day 1 | S116 | Construction of a machine learning model for predicting the characteristics from the fuel cell electrode catalyst layer preparation process | Fuel Cell Electrode Machine learning | ST-23 | 489 |
| Day 1 | H122 | Highly accurate prediction of transport phenomena in Si single crystal growth process using Hybrid-PINNs | Physics Informed Neural Networks Machine learning Crystal growth | SY-56 | 525 |
| Day 1 | YB128 | Construction of yield prediction model in catalytic cracking of vegetable oils considering fatty acid composition | catalytic cracking vegetable oil machine learning | SY-63 | 697 |
| Day 1 | YB130 | Construction of Prediction Model for the Yield of Suzuki-Miyaura Type Cross-Coupling Reactions Using Polymeric Ni Catalysts and Proposal of Novel Polymeric Ni Catalysts | Machine Learning Polymeric Ni Catalysts Chemical Reactions | SY-63 | 271 |
| Day 1 | YB132 | Application of Machine Learning in Designing the Width of Permeable Reactive Barriers Based on Reduction Mechanisms | Machine learning Permeable reactive barrier LUMO | SY-63 | 388 |
| Day 1 | YB158 | Energy-saving design of ethylbenzene production process using machine learning and Bayesian optimization | Machine learning Bayesian optimization Process design | SY-63 | 76 |
| Day 2 | YA206 | Constructing model for predicting pesticide activity using scaffolds by machine learning and visualizing the basis for prediction | Machine Learning Quantitative Structure-Activity Relationship Pesticide | SY-78 | 86 |
| Day 2 | YA214 | Use of Bayesian optimization in improving emulsion stability | machine learning emulsion surfactant | SY-78 | 729 |
| Day 2 | YA232 | Construction of predictive model for biodegradability as an alternative to biodegradability test using machine learning | Machine Learning Biodegradation plastic | SY-78 | 90 |
| Day 2 | YA266 | Measurement of adsorption equilibrium of organic compounds on poly(N-isopropylacrylamide) hydrogel and prediction using machine learning | poly(N-isopropylacrylamide) adsorption machine learning | SY-78 | 296 |
| Day 2 | YA223 | Research to Improving the Accuracy of Machine Learning Models for Predicting Reorganization Energy | Machine learning Organic semiconductor Graph Convolutional Network | SY-78 | 272 |
| Day 2 | YA285 | Definition of the domain in chemical space that discriminates the stability of a molecular structure | Machine learning Stability Molecule design | SY-78 | 194 |
| Day 2 | F208 | Predicting kinetic constants for organic pollutant degradation in US/Oxidant systems using machine learning | Ultrasonics oxidants machine learning | SY-64 | 269 |
| Day 2 | K209 | Estimation of lower explosive limit/limiting oxygen concentration of flammable gases and vapors using machine learning | Machine Learning Lower Explosive Limit Limiting Oxygen Concentration | SY-76 | 226 |
| Day 2 | L213 | Optimization of property prediction model in dynamic manufacturing process for carbon materials using a genetic algorithm | Machine learning Genetic-algorithm-based process variables and dynamics selection Multi-objective optimization | SY-66 | 273 |
| Day 2 | V213 | Development of Inspection Technique to Detect Foreign objects in Foods by using Multi-Aperture Multispectral Camera | multi-aperture multispectral camera machine learning foreign objects detection | SY-70 | 600 |
| Day 2 | R213 | [Review lecture] Property prediction with machine learning models and designs of molecules, materials and processes through inverse analysis of models | Machine learning Material design Process design | SY-51 | 431 |
| Day 2 | YB245 | Machine learning model predicting properties from monomer structures of alkyl sulfonated polyimides | alkyl sulfonated polyimide machine learning fuel cell | SY-75 | 82 |
| Day 2 | YB249 | Prediction of cell characteristics of PEFC by machine learning | Fuel cell Catalyst layer Machine learning | SY-75 | 1021 |
| Day 2 | E215 | Analysis of anion selectivity in Mg-based Layered Double Hydroxide using a Universal Neural Network Potential | machine learning layered double hydroxide (LDH) anion selectivity | ST-22 | 732 |
| Day 2 | R215 | Searching for the optimal machine learning prediction model in carbon dioxide expansion fluid density | machine learning carbon dioxide density | SY-51 | 403 |
| Day 2 | YB206 | Visualization of Energy Consumption Behavior Using Clustering-Based SHAP Value Analysis | energy system machine learning big data | SY-75 | 704 |
| Day 2 | E218 | [The Outstanding Paper Award] Optimization of metal nanoparticle synthesis conditions using automated flow system and machine learning | Nanoparticle Microreactor Machine Learning | ST-22 | 448 |
| Day 2 | L219 | Molecular design with direct inverse analysis of autoencoder-based QSAR/QSPR model | Machine learning Inverse analysis Molecular design | SY-66 | 140 |
| Day 2 | E221 | Automated reaction process analysis using data-driven approaches | Dynamic mode decomposition Chemical reaction Machine learning | ST-22 | 969 |
| Day 3 | H300 | Optimization of flow system reaction with immobilized polymer catalysts using Bayesian optimization | Polymer immobilized Pd catalyst Machine learning Process optimization | SY-65 | 552 |
| Day 3 | J301 | Controlling particle size of polyimide microparticles and modeling particle size variation using a hierarchical Bayesian approach | polyimide machine learning hierarchical Bayesian model | SY-79 | 27 |
| Day 3 | W302 | Nanomolar Scale Peptide Identification on Polydiacetylene Sensors by Hyperspectral Imaging and Machine Learning | Polydiacetytlene Hyperspectral imaging Machine learning | SY-72 | 542 |
| Day 3 | E303 | Control of foam on white water and improvement of operational efficiency by modeling a soft sensor | soft sensor machine learning factory automation | ST-22 | 95 |
| Day 3 | Q304 | [Review lecture] Future of the reactions in supercritical water and high-temperature water -Plastics development based on recycling & reaction prediction using natural language processing- | Plastic recycling Machine learning Hydrothermal | SY-73 | 1044 |
| Day 3 | YA351 | Molecular design of novelty polymer membranes using Hierarchical Variational Autoencoder | Gas separation Machine learning Polymer membrane | SY-57 | 859 |
| Day 3 | W305 | Machine learning-based cancer diagnosis using size-dependent blood residence time of nanoparticles | machine learning cancer diagnosis blood residence time | SY-72 | 624 |
| Day 3 | E306 | [Invited lecture] Autonomous Materials Search | Machine learning Autonomous Active learning | ST-22 | 145 |
| Day 3 | W308 | [Featured presentation] Predictive Modeling of Cell Viability in Extrusion-Based 3D Bioprinting using Machine Learning | Extrusion-based 3D bioprinting Cell viability Machine learning | SY-72 | 394 |
| Day 3 | Z309 | [Requested talk] Computational materials design of ternary alloys by Korringa-Kohn-Rostoker coherent potential approximation method and machine learning | first-principles study machine learning ternary alloys | HQ-15 | 516 |
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SCEJ 55th Autumn Meeting (Sapporo, 2024)
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