Electronic effects in early transition metal catalyzed olefin polymerization: challenges in featurization and...

In olefin polymerization, even seemingly simple concepts like the influence of electronic effects have sometimes eluded qualitative understanding despite 70 years of continuing research in the field. Of course, the intimate coupling of electronic and steric effects that olefin polymerization is so famous for might be simply too complex, with data science approaches - which are rapidly gaining adoption in catalysis - being the only way to solve the puzzle. Data science relies on machine-readable features or descriptors that encode essential aspects of the catalysts, and the accuracy of models depends both on the quality of data and featurization. Here, we show that some of the basic assumptions used so far (in any kind of modelling) may be flawed to the extent that they prevent accurate evaluation (and separation) of steric and electronic effects. We undertake a comprehensive analysis of the suitability of different model structures for data science approaches and analyze the performance, reliability, and data spacing of common electronic descriptors determined thereof for several group 3 and group 4 metal complexes. The insight developed in this work points not only to the complexity of the underlying chemistry being problematic but also to the inefficiency of many commonly employed descriptors in properly capturing electronic effects relevant for olefin polymerization. Recognizing the strengths and weaknesses of various approaches may help researchers select appropriate features/descriptors and better understand the scope of models beyond the initial training data.