Year: 2025
Type: article
Abstract: Kinetic Monte Carlo ( k MC) modeling is a widely used stochastic solver for simulating the time evolution of (bio)chemical processes. Its main advantage is molecule-by-molecule tracking, enabling detailed analysis of polymer kinetic systems at the distribution level, e.g., chain length or molar mass distribution (CLD or MMD). However, comparing k MC models between developers and groups remains less trivial because of the diverse implementation strategies employed. We therefore present k MC benchmark cases for free radical polymerization (FRP) and nitroxide mediated polymerization (NMP) with linear polymers, following universal guidelines. Two independent groups validate these guidelines across variations in programming language, random number generators, memory use, and data handling, with a specific emphasis on automated control volume calculation, allowing converged results based on a signal-to-noise ratio (SNR) calculation. Our work facilitates k MC adoption and fosters collaboration among researchers in the field.
Language: English
Subfield: Organic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4410237705