While MD follows the development of a system over time step-by-step, MC approaches are intrinsically stochastic. That means that probability and randomness are the core idea here. How can this lead to insights into physical systems? In probability theory there are ways to determine quantities by "smart sampling" and evaluating the remaining errors with the help of statistical analysis. The major advantage is that only part of the phase space of a system need to be evaluated. In electronic structure theory, this means that not all integrals are solved in a systematic manner (as is done in CC, CI or MP2) but instead "randomly" some of these integrals are solved and the remaining error evaluated. This approach is called quantum MC (QMC) and is one of the most accurate approaches to solving the electronic structure problem. However, for AS-ALD, this is not relevant. Instead, the stochastic description of molecular movement is very important in a process which relies on adsorbates being deposited on a surface from the gas phase in a non-directional manner. Thus, several of the following methods are heavily used in AS-ALD. It is important to notice that these MC approaches share with classical MD simulations that the electronic structure of the system is no longer considered. Furthermore, as the explicit movement of atoms is also not included in MC approaches, they represent a higher abstraction level of the underlying physics (Fig. .). As neither the electronic structure nor the movement of atoms is explicitly described, MC simulations highly rely on the quality of the input data describing the possible structures (systems states) and reactions (system transitions). The main advantage of these approaches is that they are very fast and lead to macroscopically measurable quantities (i.e., coverage).