Hybrid Stochastic Simulation for Accelerated Modeling of Free Radical and Degenerative Transfer Polymerization

Despite its accuracy, the stochastic simulation algorithm (SSA) of radical polymerization suffers from poor scalability due to the sparsity of radical species, making large‐scale simulations computationally intensive. In this work, we propose a hybrid stochastic simulation algorithm that accelerates simulations by applying Poisson‐based bulk updates to propagation and transfer events, while retaining the discrete treatment of critical steps such as initiation and termination. Compared to the traditional SSA, our method achieves up to a speedup in free radical polymerization (FRP) simulations and up to a improvement in degenerative transfer (DT) systems. Accuracy is maintained, with Jensen–Shannon divergence below 0.002 for chain length distributions. The algorithm performs particularly well under low‐radical conditions and at high monomer‐to‐initiator ratios. These results demonstrate the method' s potential to significantly accelerate stochastic modeling in polymer chemistry without compromising fidelity.