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

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