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SCEJ 54th Autumn Meeting (Fukuoka, 2023)
Program search result : machine learning : 19 programs
The preprints(abstracts) are now open (Aug. 28). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants and invited persons are required.
Keywords field exact matches “machine learning”; 19 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 | H101 | Development of machine learning model for CO2 absorption performance of blended amine solutions | CO2 absorption machine learning amine | ST-21 | 445 |
| Day 1 | PA117 | Machine learning model for predicting jet fuel fraction yield in catalytic cracking of vegetable oils | catalytic cracking sustainable aviation fuel machine learning | SY-62 | 563 |
| Day 1 | PA123 | Construction of the prediction model for graphene domain size synthesized by chemical vapor deposition | Machine Learning chemical vapor deposition graphene | SY-62 | 527 |
| Day 1 | H102 | Predicting Physical Properties of Structurally Unknown Polymers Using Spectroscopy Data | Machine Learning Predict Descriptor | ST-21 | 353 |
| Day 1 | PA117 | Machine learning model for predicting jet fuel fraction yield in catalytic cracking of vegetable oils | catalytic cracking sustainable aviation fuel machine learning | SY-62 | 563 |
| Day 1 | PA123 | Construction of the prediction model for graphene domain size synthesized by chemical vapor deposition | Machine Learning chemical vapor deposition graphene | SY-62 | 527 |
| Day 1 | H121 | Machine learning guided enzyme’s molecular recognition specificity conversion | enzyme design machine learning | ST-21 | 235 |
| Day 1 | H124 | Applicational study of symbolic regression to exploring new materials and constructing kinetics models | Machine learning Materials Informatics Reaction Kinetics | ST-21 | 948 |
| Day 2 | PA203 | Prediction of the polymer gel-solvent interaction parameter χ using support vector regression | polymer gel interaction parameter machine learning | SY-79 | 178 |
| Day 2 | PA243 | Particle size control of coacervate capsules using Bayesian optimization | machine learning microcapsule coacervation | SY-79 | 833 |
| Day 2 | PA249 | Prediction Model for Polymorphs of Coated Glycine in Particle Composites Using a Taylor Continuous Crystallizer | Crystal Polymorph Continuous Crystallization Machine Learning | SY-79 | 893 |
| Day 2 | I203 | Data Science-assisted Unveiling Comprehensive Descriptors for Electrocatalytic Anodic Reaction in Water Electrolysis on Multimetal Oxides | alkaline water electrolysis electrocatalyst machine learning | ST-23 | 565 |
| Day 2 | K203 | Design of high selective membranes for CO2 separation by machine learning and computational chemistry | Molecular simulation Machine learning Porous membrane | SY-61 | 972 |
| Day 2 | H206 | Design of integrated upstream and downstream monoclonal antibody production processes using surrogate models | Surrogate model Machine learning Bayesian optimization | ST-21 | 816 |
| Day 2 | J217 | Dynamic analysis of process operation behavior applying new informative method | Fault detection Machine learning Clustering | SY-65 | 298 |
| Day 2 | PB214 | Investigation of reactin and mass transport performance of fuel cell catalyst layers using machine learning | Fuel cell Catalyst layer Machine learning | SY-75 | 469 |
| Day 3 | T301 | New discoveries emerged from machine learning-assisted directed evolution of enzymes | Directed evolution enzyme Machine learning | SY-71 | 1027 |
| Day 3 | T303 | Machine learning-based prediction of RNA modification sites by feature combination | RNA feature combination machine learning | SY-71 | 153 |
| Day 3 | PB301 | [Requested talk] High-speed computing for powder mixing process using machine learning | Powder mixing High-speed computing Machine learning | HQ-14 | 67 |
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SCEJ 54th Autumn Meeting (Fukuoka, 2023)
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