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 propane dehydrogenation (PDH). The framework proposes multiple PDH catalysts, utilizing our laboratory-scale database. A unique five-component catalyst, 2.4Ga 2.2Pt 1.7B 1.3Zr/Al2O3, exhibits superior performance, achieving a propylene yield of 58% at 600 °C. This work highlights the excellent predictive capability of the framework and offers a new data-driven approach for developing high-performance materials for heterogeneous catalysis.
Authors
- Hyunju Chang (ORCID: https://orcid.org/0000-0001-7241-5342)
- Jungmok Oh
- Gyoung S. Na
- Yongju Yun (ORCID: https://orcid.org/0000-0001-6497-4128)
- Joonsuk Huh (ORCID: https://orcid.org/0000-0002-8792-5641)
- Namgi Jeon (ORCID: https://orcid.org/0000-0002-5981-4311)
- Hyun Woo Kim (ORCID: https://orcid.org/0000-0002-2472-2046)
- Jisu Park (ORCID: https://orcid.org/0000-0002-1772-5054)
- Junho Lee
Institutions
- Pohang University of Science and Technology (KR)
- Gwangju Institute of Science and Technology (KR)
- Institute of Nanotechnology (GB)
- Korea Research Institute of Chemical Technology (KR)
- Data Management (Italy) (IT)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- ACS Materials Letters
- Published
- 2024-10-16
- DOI
- https://doi.org/10.1021/acsmaterialslett.4c01367
- Citations
- 3
- Primary Topic
- Catalysis and Oxidation Reactions
- Type
- article
- Field-Weighted Citation Impact
- 0.32
Funders
- Korea Research Institute of Chemical Technology
- National Research Foundation of Korea