Among a variety of approaches to approximately solve the Master equation for practical systems , kinetic Monte Carlo (kMC) enjoys increasing popularity as a general and versatile methodology . As a stochastic technique, kMC works by generating an ensemble of state-to-state trajectories, whose average yields the correct time evolution. For any nontrivial system, this implies excessive queries of the possible jumps out of states i visited along a trajectory, i.e. the locations of the TSs leading out of the corresponding PES basin, as well as the concomitant rate constants k i j . In particular if the PES is evaluated by first-principles electronic structure theories like density-functional theory (DFT), the dominant solution to nevertheless keep the computational costs tractable is to determine a number of considered involved states i as well as all jumps aka the elementary processes connecting them before the actual kMC simulation. In a corresponding microkinetic model , two-sided TS searches like the climbingimage nudged-elastic band (CI-NEB) method can then efficiently be employed to comprehensively compute all barriers ∆E act i→ j and thus have the resulting list of rate constants k i j accessible in look-up tables during the kMC simulation itself. Corresponding (first-principles) kMC simulations have been successfully employed for a wide range of applications, including surface catalysis , molecular depo-sition , crystal growth and diffusion in solids . Such works typically also map the system onto a lattice model, to further reduce the number of non-equivalent elementary processes for which rate constants need to be computed as well as to exploit efficient local updating of discrete data structures and other numerical accelerators in high-end lattice kMC codes .