SCEJ 53rd Autumn Meeting (Nagano, 2022)
Program search result : Machine learning : 23 programs
The preprints can be viewed by clicking the Paper IDs.
Yellow-back sessions on Technical sessions will be On-site/online Hybrid sessions, others are Online sessions.
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Yellow-back sessions on Technical sessions will be On-site/online Hybrid sessions, others are Online sessions.
Please check notices on menu page.
Keywords field exact matches “Machine learning”; 23 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 | PB114 | Construction of Sequence-based prediction model for high expression VHH clones using machine learning | VHH machine learning production | SY-67 | 733 |
| Day 1 | CA110 | Calculation of group additivity values for cyclic silane compounds by machine learning | machine learning silane compounds quantum chemical calculation | SY-51 | 408 |
| Day 1 | DJ113 | [Review lecture] Improvement of prediction accuracy of W-ALD deposition rate by machine learning | Machine Learning ALD Simulation | ST-24 | 221 |
| Day 1 | DC116 | Machine Learning-based Multi-Objective Optimization for CO2 Membrane Separation Process | multi-objective machine learning CO2 membrane separation | SY-60 | 258 |
| Day 1 | CB122 | Estimation of temperature dependence of organic compounds solubility in water by machine learning | solubility machine learning phase equilibrium | SY-73 | 709 |
| Day 1 | PB114 | Construction of Sequence-based prediction model for high expression VHH clones using machine learning | VHH machine learning production | SY-67 | 733 |
| Day 2 | CA203 | [Featured presentation] Material design on CO2 capture system by application of machine learning using molecular descriptor combined with diffusion model | molecular descriptor CO2 capture machine learning | SY-51 | 636 |
| Day 2 | CA204 | Prediction of thermal conductivity for polymer-based composite materials using machine learning | Thermal conductivity Machine learning Polymer-based composite material | SY-51 | 91 |
| Day 2 | BA206 | Machine Learning-based Multi-Objective Optimization for CO2 Absorption Process | multi-objective machine learning CO2 absorption | SY-65 | 253 |
| Day 2 | BA208 | Data-driven decision support for equipment condition monitoring and predictive maintenance in biopharmaceutical drug product manufacturing | Digitalization Machine learning Industrial application | SY-65 | 573 |
| Day 3 | CB301 | Development of a Model for Predicting the Solubility of Organic Compounds in Supercritical CO2 Using Machine Learning Based on QSPR | Machine learning Molecular descriptors Solubility | SY-73 | 754 |
| Day 3 | PA313 | Study and analysis of clustering methods for high-dimensional "Energy Data" for building power forecasting models | energy system machine learning data science | ST-23 | 544 |
| Day 3 | PB313 | Examination of machine learning to find ion solvation extractants for Ga(Ⅲ) | solvent extraction machine learning Gallium | SY-57 | 421 |
| Day 3 | PB308 | A research for descriptors that contribute to the prediction accuracy of ion solvation extractability to Au(III) by machine learning. | machine learning solvent extraction gold | SY-57 | 259 |
| Day 3 | PA310 | Application of Neural Network Model for Valence Bond Method Calculation for Proton Conductor | solid oxide fuel cell valence bond method machine learning | ST-23 | 228 |
| Day 3 | PA312 | Analysis of high-dimensional energy data and application of regression models toward generalized electricity demand forecasting | energy system machine learning data science | ST-23 | 265 |
| Day 3 | PA320 | Prediction of an in-Plane Anomalous Current Using Numerical Simulation and Machine Learning | Modeling Machine learning Current distribution | ST-23 | 100 |
| Day 3 | DC309 | Prediction of phase equilibrium of water-organic compounds system at high-temperature and high-pressure using machine learning | machine learning prediction of phase equilibrium high-temperature and high-pressure | ST-21 | 532 |
| Day 3 | DC317 | Inverse design of polymer membrane structure for gas separation using Junction Tree VAE machine learning | machine learning polymer membrane gas separation | ST-21 | 712 |
| Day 3 | DC319 | [Featured presentation] Development of digital twin of the bulk single crystal growth of Si by using PINNs (Physics Informed Neural Networks) | Digital twin Machine learning Physics Informed Neural Networks | ST-21 | 396 |
| Day 3 | DC320 | Growth interface shape optimization and adaptive process control for InGaSb crystal growth under microgravity using machine learning | Machine Learning Reinforcement Learning Crystal Growth | ST-21 | 428 |
| Day 3 | DC322 | Effect of physics-based feature engineering in predicting product yields of catalytic cracking reactions | catalytic cracking machine learning feature engineering | ST-21 | 101 |
| Day 3 | DC324 | Discusstion on initial sample selection for Bayesian optimization of compound combinations | Bayesian optimization Machine learning Clustering | ST-21 | 328 |
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SCEJ 53rd Autumn Meeting (Nagano, 2022)
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