A hybrid method for micro-mesoscopic stochastic simulation of reaction-diffusion systems
Alireza Sayyidmousavi1, Katrin Rohlf1, Silvana Ilie1
1Department of Mathematics, Ryerson University, 350 Victoria St, M5B 2K3 Toronto, Canada.
A novel hybrid algorithm combines Reactive Multi-Particle Collision (RMPC) dynamics and Reaction-Diffusion Master Equation (RDME) for efficient simulation of reaction-diffusion systems, offering significant speed-ups.
Area of Science:
- Computational chemistry
- Chemical kinetics
- Multiscale modeling
Background:
- Accurate simulation of reaction-diffusion systems is crucial in various scientific fields.
- Existing molecular-based methods can be computationally expensive.
- Bridging micro and meso scales presents a significant modeling challenge.
Purpose of the Study:
- To introduce a novel micro-meso hybrid algorithm for reaction-diffusion systems.
- To leverage Reactive Multi-Particle Collision (RMPC) dynamics at the microscopic level.
- To utilize the Reaction-Diffusion Master Equation (RDME) at the mesoscopic level.
Main Methods:
- Developed a hybrid algorithm integrating Ghost Cell Method, RMPC, and RDME.
- Employed the Inhomogeneous Stochastic Simulation Algorithm to solve the RDME.
- Tested the algorithm on three distinct reaction-diffusion systems.
Main Results:
- Achieved excellent agreement between the hybrid algorithm's results and deterministic solutions.
- Demonstrated that RMPC dynamics can effectively model particle trajectories and reactions.
- Showcased significant computational speed-ups through proper domain discretization compared to full RMPC.
Conclusions:
- The proposed micro-meso hybrid algorithm provides an efficient and accurate approach for simulating reaction-diffusion systems.
- The integration of RMPC dynamics offers advantages in speed and conservation properties.
- Domain discretization is a key factor in optimizing the performance of the hybrid algorithm.
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