Kinetic Monte-Carlo Algorithms for Active-Matter systems

We study kinetic Monte-Carlo (KMC) descriptions of active particles. By relying on large discrete time steps, KMC algorithms accelerate the relaxational dynamics of active systems towards their steady-state. We show, however, that their continuous-time limit is ill-defined, leading to the vanishing of trademark behaviors of active matter such as the motility-induced phase separation, ratchet effects, as well as to a diverging mechanical pressure. We show how mixing passive steps with active ones regularizes this behavior, leading to a well-defined continuous-time limit. We propose new AKMC algorithms whose continuous-time limits lead to the active dynamics of Active-Ornstein Uhlenbeck, Active Brownian, and Run-and-Tumbles particles.