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Ghost‐Mirror Approach for Accurate and Efficient Kinetic Monte Carlo Simulation of Seeded Emulsion Polymerization
Author(s) -
Tripathi Amit K.,
Tsavalas John G.
Publication year - 2020
Publication title -
macromolecular theory and simulations
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.37
H-Index - 56
eISSN - 1521-3919
pISSN - 1022-1344
DOI - 10.1002/mats.202000033
Subject(s) - consistency (knowledge bases) , particle (ecology) , monte carlo method , seeding , phase (matter) , kinetic energy , emulsion polymerization , kinetic monte carlo , polymerization , materials science , statistical physics , aqueous solution , work (physics) , computer simulation , volume (thermodynamics) , aqueous two phase system , chemical physics , chemistry , computer science , thermodynamics , physics , simulation , mathematics , classical mechanics , composite material , statistics , artificial intelligence , oceanography , polymer , organic chemistry , geology
The kinetic Monte Carlo (kMC) method is well suited to the simulation of seeded emulsion polymerization reactions. Inadequate simulation volume results in an incorrect radical entry rate, and as such that inaccuracy propagates into the particle phase simulation. A novel kMC method is described here that has coined the Ghost‐Mirror approach (GM‐kMC) to guarantee sufficient aqueous phase simulation volume while minimizing the number of dispersed phase environments, and while maintaining accuracy and consistency with results produced by kMC simulations. To do so, a subset of a kMC method is replicated where only the aqueous phase reactions are treated in that extended volume; particle phases in those replicated volumes are untreated, as “ghosts”. In doing so, a critical condition for aqueous phase simulation volume is calculated to yield accurate results in the overall system. Important to emphasize though is that one does not require the equivalent number of simulated particles in the GM‐kMC approaches. The required number of particles to simulate accurate results can be even orders of magnitude smaller than the kMC approach; inherently offering significant gains in computational efficiency. This is extended further, reducing the required simulation time, by a hybrid extension to this GM‐kMC method.