Catalytic Performance of Cycloalkyl-Fused Aryliminopyridyl Nickel Complexes toward Ethylene Polymerization by QSPR Modeling

Meraz, Mostakim; Malik, Arfa Abrar; Yang, Wenhong; Sun, Wen‐Hua

doi:10.3390/catal11080920

Cited by 3 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The findings showed that the bulky phenyl ring substituents and the net charges on the core metal and N atoms were favorable for the catalytic activity [19]. Subsequently, 2D-and 3D-QSPR were employed to examine the different catalytic performances of cycloalkyl-fused aryliminopyridyl nickel complexes, and the 3D contour maps depicted that smaller sizes of cycloalkyl-fused rings favored catalytic activity while hindering an increase in molecular weight [20].…”

Section: Introductionmentioning

confidence: 99%

Catalytic Activity Prediction of α-Diimino Nickel Precatalysts toward Ethylene Polymerization by Machine Learning

Abbas,

Meraz,

Yang

et al. 2024

Catalysts

Self Cite

View full text Add to dashboard Cite

The present study explored machine learning methods to predict the catalytic activities of a dataset of 165 α-diimino nickel complexes in ethylene polymerization. Using 25 descriptors as the inputs, the XGBoost model presented the optimal performance among six different algorithms (R2 = 0.999, Rt2 = 0.921, Q2 = 0.561). The results of the analysis indicate that high activity is related to the presence of polarizable atoms and less bulky substituents within the N-aryl group. This approach offers valuable insights on the variation principle of catalytic activity as a function of complex structure, helping to effectively design and optimize α-diimino Ni catalysts with desirable performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Catalytic Activity Prediction of α-Diimino Nickel Precatalysts toward Ethylene Polymerization by Machine Learning

Abbas,

Meraz,

Yang

et al. 2024

Catalysts

Self Cite

View full text Add to dashboard Cite

show abstract

Thermally stable unsymmetrical 2,3-diiminobutane-nickel pre-catalysts for synthesis of high molecular weight polyethylene elastomers

Li,

Qin,

Mahmood

et al. 2023

European Polymer Journal

View full text Add to dashboard Cite

Application and prospect of machine learning in polyolefin catalysts

Yang¹,

Sun²

2022

Chin. Sci. Bull.

Self Cite

View full text Add to dashboard Cite

Industrial and academic research has been extensively inspired by the ever-growing demand of polyolefin with high performance due to its special physical and mechanical properties, which has been widely applied in the area of engineering plastics, elastomer and high grade lubricants. Transition metal complex catalysts, which can make the olefin polymerization reaction feasible, have been one of the key techniques to produce polyolefin with various structures and properties. Although many fruitful reports are available describing different attempts on enhancing the performance of polyolefin catalyst by the means of the alteration of the ligand framew orks, shuffling the substituents as well as introducing of new ligands. Nevertheless, the traditional process of catalyst development, using the trial-and-error method, usually needs long experimental steps and period s. Meanwhile, the measurement of catalytic performance is high cost and needs a lot of resources as well. Machine learning, as the core strategy of artificial intelligence, has shown strong predictive power in many fields of science and technology. However, the application in chemistry, especially in catalysis, is still in its infancy. Relying on the rapid development of different a lgorithms and computer hardware, it is the right time to harvest the potential of machine learning in the field of catalysis across academy and industry. Herein, we discuss the recent advances in the field of polyolefin catalysts by using machine learning methods, including Ziegler-Natta catalysts, phosphine monocyclic imine Cr(P,N) catalysts, ansa zirconocene catalysts, and late-transition metal complex catalysts. The catalytic performance is well predicted, providing the insight into the underlying mechanism of relationship between the micro-structure of catalyst and its macro-performance at the molecule level.Tracing the recent progress, the report of machine learning in polyolefin catalysts is relatively few. One of the main reasons is that the experimental study of catalytic performance is time -consuming and labor-intensive and it is difficult to establish a big data set from kinetic studies. However, the recent work by Cavallo suggests that the model with good prediction and validation results can still be obtained, even though the model catalysts in data set are selected from different laboratories. This may inspire researchers from different groups to contribute their experimental data together and share with each other. On the other hand, compared with supervised machine learning, the unsupervised methods have the advantage of low dependence on experimental data and may effectively solve the problem of building large data set. To the best of our knowledge, modern statistical learning techniques will be a strong tool for computational optimization and discovery. This perspective provides a platform for an integrate d machine learning technique toward new design in the field of polyolefin catalysts, which is expected to change the traditional way by lowering the cost ...

show abstract

Catalytic Performance of Cycloalkyl-Fused Aryliminopyridyl Nickel Complexes toward Ethylene Polymerization by QSPR Modeling

Cited by 3 publications

References 48 publications

Catalytic Activity Prediction of α-Diimino Nickel Precatalysts toward Ethylene Polymerization by Machine Learning

Catalytic Activity Prediction of α-Diimino Nickel Precatalysts toward Ethylene Polymerization by Machine Learning

Thermally stable unsymmetrical 2,3-diiminobutane-nickel pre-catalysts for synthesis of high molecular weight polyethylene elastomers

Application and prospect of machine learning in polyolefin catalysts

Contact Info

Product

Resources

About