Article ; Online: Predicting the catalytic activities of transition metal (Cr, Fe, Co, Ni) complexes towards ethylene polymerization by machine learning.
Journal of computational chemistry
2023 Volume 45, Issue 11, Page(s) 798–803
Abstract: The study aims to execute machine learning (ML) method for building an intelligent prediction system for catalytic activities of a relatively big dataset of 1056 transition metal complex precatalysts in ethylene polymerization. Among 14 different ... ...
Abstract | The study aims to execute machine learning (ML) method for building an intelligent prediction system for catalytic activities of a relatively big dataset of 1056 transition metal complex precatalysts in ethylene polymerization. Among 14 different algorithms, the CatBoost ensemble model provides the best prediction with the correlation coefficient (R |
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Language | English |
Publishing date | 2023-12-21 |
Publishing country | United States |
Document type | Journal Article |
ZDB-ID | 1479181-X |
ISSN | 1096-987X ; 0192-8651 |
ISSN (online) | 1096-987X |
ISSN | 0192-8651 |
DOI | 10.1002/jcc.27291 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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