Data-Driven Exploration of the Polyethylene Catalyst Chemical Space via Machine Learning.
TL;DR
A data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation with large-scale virtual library generation is presented, establishing a practical route from experimental data to actionable catalyst designs.
Abstract
The discovery of highly active polyethylene (PE) catalysts demands a systematic understanding of structure-condition-activity relationships in a vast chemical space. In this Letter, we present a data-driven framework combining explainable machine learning (ML) with large-scale virtual library generation. From a curated data set of 507 catalysts (bis(phenoxyimine) and bis(imino)pyridine ligands, seven metals), a gradient boosting regression (GBR) model achieves a test R2 of 0.91, outperforming convolutional and graph neural networks. SHAP analysis identifies topological (Chi2v), electronic (EState_VSA), and hydrophobic (SlogP_VSA) descriptors as governing activity and reveals a classical volcano-type temperature dependence, fundamentally governed by the Sabatier principle. A virtual library of 665 685 structures, constructed via combinatorial fragment assembly, extends the known chemical space substantially. High-throughput screening, coupled with SCscore filtering, yields 1090 synthetically accessible candidates with predicted activities exceeding 2 × 107 g mol-1 h-1. Substructure analysis uncovers metal-dependent design rules, in which early transition metals favor electron-deficient aromatics while late metals profit from moderately sized alkyls. This work establishes a practical route from experimental data to actionable catalyst designs.