SCEJ 87th Annual Meeting (Kobe, 2022)
Last modified: 2022-03-04 12:00:00
Program search result : Machine learning : 11 programs
Program of CS-2 is updated.
The preprints are now open (Mar. 2nd). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants in Period I/II and invited persons are required. (The participants registered in Period III will get the ID/PW on Mar. 15th.)
The yellow back on the Technical sessions page denotes streaming-live session. (HQ-21 is changed to online.)
The preprints are now open (Mar. 2nd). These can be viewed by clicking the Paper IDs. The ID/PW sent to the Registered participants in Period I/II and invited persons are required. (The participants registered in Period III will get the ID/PW on Mar. 15th.)
The yellow back on the Technical sessions page denotes streaming-live session. (HQ-21 is changed to online.)
Keywords field exact matches “Machine learning”; 11 programs are found.
The search results are sorted by the start time.
| Time | Paper ID | Title / Authors | Keywords | Topic code | Ack. number |
|---|---|---|---|---|---|
| Day 1 | C123 | [Invited lecture] Nanoporous materials informatics: How data science can accelerate the exploratory search of efficient nanoporous (electro)catalysts | Zeolite Nanoporous materials Machine learning | K-1 | 310 |
| Day 2 | PB238 | Prediction of adsorption amount of aromatic hydrocarbons in organoclay by machine learning | organoclay machine learning adsorption | 4-e | 441 |
| Day 2 | I217 | Machine learning guided cofactor specificity conversion of malic enzyme | enzyme design cofactor machine learning | 7-f | 625 |
| Day 2 | PC234 | Study of perovskite-type proton conductor search by applying machine learning to valence bond method | solid oxide fuel cell valence bond method machine learning | 9-e | 347 |
| Day 2 | B218 | Use of machine learning and feature engineering for product composition prediction in heavy oil catalytic cracking reactions | machine learning feature engineering catalytic cracking | CS-1 | 484 |
| Day 2 | B219 | Prediction of organic compound solubility for subcritical water by machine learning | machine learning subcritical water solubility | CS-1 | 577 |
| Day 3 | PD367 | Construction of product yield prediction model using machine learning in co-processing of bio-oil and heavy oil in fluid catalytic cracking | bio-oil co-processing machine learning | 5-a | 561 |
| Day 3 | M303 | Future prospects in AI technologies for process control | process control artificial intelligence machine learning | 6-d | 63 |
| Day 3 | I314 | Antiviral Catechin cocrystallization prediction by machine learning | cocrystallization machine learning catechin | 1-b | 442 |
| Day 3 | D317 | Machine learning based analysis of catalytic lignin depolymerization processes | Lignin depolymerization Machine learning Catalysts | IS-1 | 208 |
| Day 3 | PE362 | Inverse design of polymeric materials using the latent space of Junction Tree VAE | Junction Tree Variational Autoencoder Machine learning | 12-j | 631 |
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SCEJ 87th Annual Meeting (Kobe, 2022)
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