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SCEJ 52nd Autumn Meeting (Okayama, 2021)
Program search result : 学習 : 22 programs
ST-25,SY-56,SY-64,SY-65,SY-70 are changed from live streaming sessions to online sessions.
The preprints are now open (Sep. 8). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants (excludes free registration) and invited persons are required.
The preprints are now open (Sep. 8). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants (excludes free registration) and invited persons are required.
Title (J) field includes “学習”; 22 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 | PA104 | Application of machine learning to prediction and improvement of target product yields in catalytic cracking of triglyceride | catalytic cracking triglyceride machine learning | SY-62 | 778 |
| Day 1 | VF104 | Prediction of Solubility of Organic Compounds in Supercritical Carbon Dioxide by Artificial Neural Network (ANN) with Molecular Descriptors | Supercritical carbon dioxide Solubility Molecular descriptors | SY-51 | 704 |
| Day 1 | LD106 | Hybrid Plug-Flow Reactor model applying machine learning | Plug-flow reactor Machine learning Ammonia synthesis | SY-65 | 83 |
| Day 1 | LD107 | Analysis of phase behavior of phase separation CO2 absorbent using machine learning by molecular information | CO2 capture phase separation absorbent machine learning | SY-65 | 641 |
| Day 1 | VG108 | Flow simulation of polymeric liquids with learned constitutive relation | non-Newtonian fluid Machine Learning CFD | SY-52 | 587 |
| Day 1 | VF109 | Application of machine learning from molecular information and data processing for system design of liquified phase by high-pressure CO2 | molecular information solid-liquid-vapor equilibrium machine learning | SY-51 | 636 |
| Day 1 | VN109 | Analysis of amide compound reaction in subcritical water by machine learning | Subcritical water Machine learning Protein | SY-73 | 805 |
| Day 1 | PA104 | Application of machine learning to prediction and improvement of target product yields in catalytic cracking of triglyceride | catalytic cracking triglyceride machine learning | SY-62 | 778 |
| Day 1 | LE115 | [Featured presentation] Machine learning-based screening of storage protein-derived bioactive peptides for oral intake | Machine learning Peptides Screening | SY-71 | 170 |
| Day 1 | VM115 | [Featured presentation] Machine learning-mediated analysis and design for microbial growth media | machine learning fermentation bioprocess | SY-69 | 144 |
| Day 1 | PB103 | Stress prediction of particle structure by numerical simulation and machine learning | Stress distribution DEM Machine learning | ST-24 | 874 |
| Day 1 | PB119 | 3D Generation and Reconstruction of the Fuel Cell Catalyst Layer using 2D Images based on Deep Learning | Fuel Cell Catalyst Layer Deep Learning 3D generation | ST-24 | 878 |
| Day 2 | PB259 | Machine learning and a high-throughput cultivation for design of growth media in a heterologous protein production | Escherichia coli Green fluorescence protein Machine learning | SY-67 | 17 |
| Day 2 | VN201 | [Requested talk] Development of fast chemical process using high pressure fluids: Continuous extraction and machine learning | supercritical extraction machine learning | SY-73 | 192 |
| Day 2 | VN203 | Prediction of organic compound solubility in subcritical water by machine learning | Solubility Subcritical water Machine learning | SY-73 | 679 |
| Day 2 | PB231 | Prediction of Cofactor specificity of malic enzyme using machine learning | Enzyme Machine learning Cofactor | SY-67 | 429 |
| Day 2 | PB231 | Prediction of Cofactor specificity of malic enzyme using machine learning | Enzyme Machine learning Cofactor | SY-67 | 429 |
| Day 2 | PB259 | Machine learning and a high-throughput cultivation for design of growth media in a heterologous protein production | Escherichia coli Green fluorescence protein Machine learning | SY-67 | 17 |
| Day 2 | LA218 | [Invited lecture] Development of Simulation based on Deep Learning and Its Application to Material Exploration | Material Exploration Deep Learning | SV-1 | 42 |
| Day 3 | PA306 | A trial study on machine learning to develop ion solvation extractants for Au(III) | machine learning extraction Au(III) | SY-57 | 117 |
| Day 3 | LE313 | [Invited lecture] Machine-learning-assisted simultaneous multiparameter screening in flow synthesis | Bayesian optimization Gaussian process regression Categorical parameter | SY-63 | 223 |
| Day 3 | LG319 | Development of an automatic flow synthesis system for nanoparticles and analysis of synthesis conditions by machine learning | Nanoparticle Microreactor Machine Learning | SY-77 | 670 |
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SCEJ 52nd Autumn Meeting (Okayama, 2021)
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