Prediction of catalytic activities of bis(imino)pyridine metal complexes by machine learning
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.
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Interrogation of the key descriptors, even in the case of a limited data set, can provide further insight into what maximizes catalytic activity.
“Importantly, interrogation of the key descriptors, even in the case of a limited data set, can provide further insight into what maximizes catalytic activity (89).”
From Machine Learning for the Discovery, Design, and Engineering of Materials. (2022) · cites this paper as [89]
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Machine Learning for the Discovery, Design, and Engineering of Materials.
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- Authors
- Yang, W., Fidelis, T. T., & Sun, W.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Yang, Wenhong
- Sun, Wen‐Hua
- Venue
- Journal of Computational Chemistry
- Publisher
- Wiley
- Published
- 2020-02-05
- Volume
- 41
- Issue
- 11
- Pages
- 1064-1067
- ISSN
- 0192-8651 (ISSN-L)
- 1096-987X
- OpenAlex
- W3005261377
- Cited by
- 17
- References
- 20
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 2 · 2021: 2
- FWCI
- 0.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Computational Chemistry (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (21871275)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Organometallic Complex Synthesis and Catalysis
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- ethylene polymerization, catalytic activity prediction, artificial neural networks, molecular descriptors, Boruta feature selection, bulky substituents, catalyst design
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