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SCEJ 51st Autumn Meeting (2020)

Program search result : Machine learning : 9 programs

All sessions can be attended from the On-line (Virtual) Meeting Site.
Preprints(Abstracts) are now open. Click the Paper IDs. (Registered participants and invited persons only)
The ID/PW was sent on Sept. 10 (for earlybird registered participants) or on Sept. 23 (for on-site registered participants).
(Aug. 8) Flash session of SY-69 has been cancelled.
(Aug. 24,27) Schedule of SY-74 (X306, X307) and HQ-11 (D301) has been changed.

Keywords field exact matches “Machine learning”; 9 programs are found. (“Poster with Flash” presentations are double-counted.)
The search results are sorted by the start time.

TimePaper
ID
Title / AuthorsKeywordsTopic codeAck.
number
Day 1
13:2014:00
E114[Invited lecture] Catalyst Informatics Approach for the Prediction of Catalytic Reaction Yields
Catalyst Informatics
Machine Learning
Catalyst Informatics
SY-8081
Day 1
14:4015:40
PB109Estimation of structural properties of porous electrode layer by simulation, observation and machine learning
porous electrode layer
machine learning
image processing
ST-24880
Day 1
15:2015:40
Y120Prediction of liquid-liquid equilibria using activity coefficient model with molecular information and machine learning
liquid-liquid equilibrium
molecular information
machine learning
SY-51604
Day 1
16:4017:40
PB142Multiple regression analysis of electricity demand with machine learning for grid synchronization distributed generation system
distributed generation
renewable energy
machine learning
ST-24732
Day 2
11:0011:20
T207Development of polymer blends using machine learning
Machine learning
Polymer composite
SY-67282
Day 2
12:4014:00
PB236Investigating methods of a medium evaluation system by machine learning.
Medium
Machine learning
component analysis
SY-69621
Day 2
13:4014:00
Y215Statistical thermodynamics database construction to search for novel ionic liquids with gas absorption
Ionic liquid
Database
Machine learning
SY-51567
Day 3
9:4010:00
X303Estimation of the solubility of biomass derived organic compounds in high-temperature and high-pressure water
Biomass
Solubility
Machine learning
SY-74857
Day 3
11:2011:40
I308Application of machine learning to product composition prediction in catalytic cracking reaction
machine learning
feature engineering
catalytic cracking
SY-63533

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SCEJ 51st Autumn Meeting (2020)


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