The era of data science is clearly still on the uprise and the first applications of methods derived from machine learning approaches entered the field of AS-ALD. However, the usage is yet scarce. Aside from the already mentioned possibility to use MLPs in dynamic modelling of selective deposition, ML approaches could also be used to reveal correlations of microscopic quantities computed by ab initio modelling with experimental observables like growth per cycle. Also, macroscopic properties like vapor pressure could be accessible via ML. Modern ML techniques like active learning or reinforcement learning have not yet been applied to the field of AS-ALD.