The Tenth q-bio Summer School - Albuquerque: Lecture 8

Introduction to Kinetic Monte Carlo

Danny Perez

Theoretical Division (T-1)

Los Alamos National Laboratory

Despite the constant increase in available computing power, the computational prediction of the long-time behavior of atomistic systems in an ongoing challenge across many fields, including biology, materials science, and chemistry. One common strategy to address this challenge is to coarse-grain the system and to represent the dynamics as a so-called Markov process defined in terms of discrete states and transitions. Realizations of possible trajectories in this coarse space can then be obtained with an extremely efficient stochastic algorithm called Kinetic Monte Carlo (KMC). In this talk, I will discuss various aspects of the coarse graining problem (defining states, computing transition rates, etc.), discuss some fundamental properties of Markov processes, and discuss the generation of trajectories using KMC.