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Prediction of catalytic activities of bis(imino)pyridine metal complexes by machine learning.

Authors
  • Yang, Wenhong1, 2
  • Fidelis, Timothy Tizhe1, 2
  • Sun, Wen-Hua1, 2
  • 1 Key Laboratory of Engineering Plastics and Beijing National Laboratory for Molecular Science, Institute of Chemistry, Chinese Academy of Sciences, Beijing, China. , (China)
  • 2 University of Chinese Academy of Sciences, Beijing, China. , (China)
Type
Published Article
Journal
Journal of Computational Chemistry
Publisher
Wiley (John Wiley & Sons)
Publication Date
Apr 30, 2020
Volume
41
Issue
11
Pages
1064–1067
Identifiers
DOI: 10.1002/jcc.26160
PMID: 32022293
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

This work demonstrates the potential of machine learning (ML) method to predict catalytic activity of transition metal complex precatalyst toward ethylene polymerization. For this purpose, 294 complexes and 15 molecular descriptors were selected to build the artificial neural network (ANN) model. The catalytic activity can be well predicted by the obtained ANN model, which was further validated by external complexes. Boruta algorithm was employed to explicitly decipher the importance of descriptors, illustrating the conjugated bond structure, and bulky substitutions are favorable for catalytic activity. The present work indicates that ML could give useful guidance for the new design of homogenous polyolefin catalyst. © 2020 Wiley Periodicals, Inc.

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