Machine learning and text mining approaches to design selective catalyst reduction synthesis routes
The workflow for designing SCR synthesis routes employs machine learning and text mining techniques, including data extraction, feature encoding and filtering, model training and interpretation, and predicting synthesis routes.
Authors
- Shaorui Sun (ORCID: https://orcid.org/0000-0001-5891-1956)
- Chenyu Huang
- Yunjiang Zhang (ORCID: https://orcid.org/0000-0002-4361-9685)
- Jing Li (ORCID: https://orcid.org/0000-0003-4602-3227)
- Shuyuan Li (ORCID: https://orcid.org/0000-0001-8656-1500)
Institutions
- Nanjing Institute of Technology (CN)
- Beijing University of Technology (CN)
- Beijing University of Chemical Technology (CN)
Publication Details
- Journal
- Catalysis Science & Technology
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4cy01159g
- Citations
- 3
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 1.55
Funders
- National Science Foundation
- National Natural Science Foundation of China