Acceleration scheme for particle transport in kinetic Monte Carlo methods

Area of Science:

• Computational materials science
• Chemical engineering
• Organic electronics

Background:

• Kinetic Monte Carlo (kMC) simulations are vital for understanding (electro-)chemical processes, linking atomistic properties to macroscopic behavior.
• Computational demands increase significantly with large time disparities in competing processes, necessitating acceleration algorithms.
• Existing accelerated superbasin kMC (AS-kMC) methods face computational overhead due to runtime superbasin computation and large databases.

Purpose of the Study:

• To propose a novel acceleration scheme for diffusion and transport processes within kMC simulations.
• To address the computational challenges posed by large time disparities in kMC simulations.
• To improve the efficiency of kMC simulations for systems like organic semiconductors.

Main Methods:

• Developed a new acceleration scheme that detects critical superbasins during system initialization.
• Derived scaling factors for critical rates and a lower bound for sighting counts within superbasins.
• Applied the scheme to a 1D-chain example and to study time-of-flight (TOF) in organic semiconductors.

Main Results:

• The proposed algorithm demonstrates superior performance over AS-kMC in simulation time.
• Achieved significant acceleration (up to 65x) in TOF studies of organic semiconductors.
• Maintained negligible error in TOF values while minimizing computational overhead, as superbasins are computed only once.

Conclusions:

• The novel acceleration scheme effectively overcomes computational limitations in kMC simulations with large time disparities.
• This method offers a computationally efficient and accurate approach for studying transport phenomena in materials like organic semiconductors.
• The pre-computation of superbasins significantly reduces runtime overhead, making it suitable for complex systems.