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The aim is to enhance reaction path exploration and product identification in alkene polymerization using a machine learning framework.

Toward Reaction Vessel Mimicry: Machine Learning-Assisted Automated Exploration of Alkene Polymerization and Its Transferability

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The aim is to enhance reaction path exploration and product identification in alkene polymerization using a machine learning framework.
Utilized semiempirical methods for initial exploration followed by DFT for accuracy refinement.
Developed a delta-learning neural network potential (ΔNNP) for automated reaction path exploration.
Applied the ΔNNP to model ethylene polymerization with the [ZrCp2CH3]+ catalyst.
Trained the model on reaction path networks involving one and two ethylene molecules and extended to six ethylene molecules.
Investigated the impact of a cocatalyst on the reaction network through iterative training.
Achieved DFT-level accuracy in reaction kinetics and product prediction through ΔNNP.
Successfully captured initiation, propagation, and termination steps in ethylene polymerization.
Established a foundation for propylene polymerization using a minimally trained model.
Identified several new metallacycle structures with minimal additional training.
Enabled handling of complex reactions involving repeated analogous steps.

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Automated exploration predicts reaction outcomes in alkene polymerization, suggesting improved methodology for accurate modeling.

Ghorai et al. (2026) studied this question.

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