Data-Driven Development of Heterogeneous Catalysts for Propane Dehydrogenation with Machine Learning and Metaheuristic Optimization
Source abstract
Abstract
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/Al 2 O 3, 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.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Catalysis and Oxidation Reactions · Catalytic Processes in Materials Science · Machine Learning in Materials Science
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Chemical Engineering
Pohang University of Science and Technology (POSTECH)
Department of Chemistry
Sungkyunkwan University (SKKU)
SKKU Advanced Institute of Nanotechnology (SAINT)
Sungkyunkwan University
Chemical Data-driven Research Center
Korea Research Institute of Chemical Technology (KRICT)
Institute of Quantum Biophysics
Gwangju Institute of Science and Technology (GIST)
Funding
Korea Research Institute of Chemical Technology · National Research Foundation of Korea
References (40)
Show source-provided references
1. 10.1038/s41929-019-0298-310.1038/s41929-019-0298-3 ↗
2. 10.1002/aic.1619810.1002/aic.16198 ↗
3. 10.1021/cr500243610.1021/cr5002436 ↗
4. 10.1016/j.cej.2017.06.16110.1016/j.cej.2017.06.161 ↗
5. 10.1016/j.commatsci.2022.11183310.1016/j.commatsci.2022.111833 ↗
6. 10.1038/s41929-018-0150-110.1038/s41929-018-0150-1 ↗
7. 10.1021/acsomega.9b0367310.1021/acsomega.9b03673 ↗
8. 10.1016/j.cattod.2020.07.07410.1016/j.cattod.2020.07.074 ↗
9. 10.1039/D0CS00814A10.1039/D0CS00814A ↗
10. 10.1016/j.cej.2014.02.08410.1016/j.cej.2014.02.084 ↗
11. 10.1016/j.cej.2022.13586010.1016/j.cej.2022.135860 ↗
12. 10.1016/j.swevo.2021.10088510.1016/j.swevo.2021.100885 ↗
13. 10.1016/S0926-860X(00)00643-810.1016/S0926-860X(00)00643-8 ↗
14. 10.1016/j.apcata.2021.11803610.1016/j.apcata.2021.118036 ↗
15. 10.1021/acscatal.2c0324010.1021/acscatal.2c03240 ↗
16. 10.1002/anie.20140446010.1002/anie.201404460 ↗
17. 10.1021/acscatal.9b0554810.1021/acscatal.9b05548 ↗
18. 10.1021/ie900534m10.1021/ie900534m ↗
19. 10.1021/acscatal.6b0128610.1021/acscatal.6b01286 ↗
20. 10.1021/acscatal.0c0278210.1021/acscatal.0c02782 ↗
21. 10.1039/C4NR02143F10.1039/C4NR02143F ↗
22. 10.1021/acscatal.0c0158010.1021/acscatal.0c01580 ↗
23. 10.1002/cplu.20220040510.1002/cplu.202200405 ↗
24. 10.1006/jcat.1999.241310.1006/jcat.1999.2413 ↗
25. 10.1016/j.jcat.2018.06.02810.1016/j.jcat.2018.06.028 ↗
26. 10.1016/S0926-860X(01)00656-110.1016/S0926-860X(01)00656-1 ↗
27. 10.1016/j.jcat.2009.03.00410.1016/j.jcat.2009.03.004 ↗
28. 10.1038/s41467-023-41341-310.1038/s41467-023-41341-3 ↗
29. 10.1038/s41467-024-50215-110.1038/s41467-024-50215-1 ↗
30. 10.1039/D0RE00378F10.1039/D0RE00378F ↗
31. 10.1016/j.amc.2009.03.09010.1016/j.amc.2009.03.090 ↗
32. 10.1016/j.engappai.2022.10531110.1016/j.engappai.2022.105311 ↗
33. 10.1038/s41467-020-16693-910.1038/s41467-020-16693-9 ↗
34. 10.1021/acscatal.1c0255310.1021/acscatal.1c02553 ↗
35. 10.1021/acscatal.6b0032910.1021/acscatal.6b00329 ↗
36. 10.1016/0021-9517(87)90372-110.1016/0021-9517(87)90372-1 ↗
37. 10.1016/0039-6028(89)90156-810.1016/0039-6028(89)90156-8 ↗
38. 10.1021/acsami.0c2286510.1021/acsami.0c22865 ↗
39. 10.1039/D3SC04711C10.1039/D3SC04711C ↗
40. 10.1021/acscatal.5b0291710.1021/acscatal.5b02917 ↗