Authors: Wenhong Yang | Wen‐Hua Sun
Source: Chinese Science Bulletin (Chinese Version) (article, 2022-02-18)
Field: Materials Science — Materials Chemistry

Application and prospect of machine learning in polyolefin catalysts <p indent="0mm">Industrial and academic research has been extensively inspired by the ever-growing demand for 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 frameworks, shuffling the substituents as well as introducing new ligands. Nevertheless, the traditional process of catalyst development, using the trial-and-error method, usually needs long experimental steps and periods. 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 algorithms 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, <italic>ansa</italic> zirconocene catalysts, and late-transition metal complex catalysts. The catalytic performance is well predicted, providing insight into the underlying mechanism of the relationship between the micro-structure of the 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 integrated machine learning technique for new design in the field of polyolefin catalysts, which is expected to change the traditional way by lowering the cost associated with the initial discovery. It is anticipated that machine learning will become the signal of a high-level computational tool in the catalytic community in the near future. Polyolefin ; Catalysis ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Carbon dioxide utilization in catalysis