Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization

Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization

Recent advances in data-driven approaches using the machine learning (ML) method have enabled the discovery of high-performance materials. This paper presents a hybrid framework that combines ML models with a metaheuristic optimization algorithm, to explore improved heterogeneous catalysts for propa...

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Bibliographic Details
Main Author: Jungmok Oh (14117464) (author)
Other Authors: Junho Lee (220469) (author), Jisu Park (821962) (author), Namgi Jeon (14117461) (author), Gyoung S. Na (8367210) (author), Hyunju Chang (2166736) (author), Joonsuk Huh (1640263) (author), Hyun Woo Kim (1543174) (author), Yongju Yun (1780279) (author)
Published: 2024
Subjects:
Medicine
Genetics
Pharmacology
Space Science
Biological Sciences not elsewhere classified
Mathematical Sciences not elsewhere classified
Information Systems not elsewhere classified
metaheuristic optimization algorithm
excellent predictive capability
600 ° c
driven approaches using
combines ml models
2 </ sub
driven development
driven approach
work highlights
unique five
scale database
propylene yield
propane dehydrogenation
performance materials
pdh ).
paper presents
machine learning
heterogeneous catalysts
heterogeneous catalysis
component catalyst
7b 1
4ga 2
2pt 1
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