ML is a subfield of artificial intelligence (AI) where statistical methods are used to enable computers to perform tasks they were not explicitly programmed for. It is a part of data-based science and thus fundamentally different from the other methods outlined above which are based on physical principles and solving the resulting equations. Thus, ML-approaches require a large set of (reliable) data to be trained on. For a given problem, a model is trained on these data sets which then fulfills the targeted task. This very fast developing field is summarized in recent reviews. Beyond the usage of ML for creating interatomic potentials as outlined in section 2.4.3, this family of approaches can also be used to extract information from large datasets and interpolate data. For instance, ideal experimental ALD parameters have been predicted in the past. Overall, the usage of ML techniques is still rare in AS-ALD. The major bottleneck for widespread usage of ML in AS-ALD is the lack of large datasets for training ML models from experimental as well as from the computational side.