One advantage of computational modeling in AS-ALD is the treatment of large datasets without the need to optimize process conditions for every precursor. Thus, screening approaches have been developed over the years where computational modeling was used to select precursors and/or inhibitors for a given task of selective deposition from a large set of possible candidates. The question of precursor chemistry and precursor choice has been discussed in the ALD literature quite intensively. Recently, a combination of molecular DFT and ML approaches has been used to predict precursor reactivity for different surface terminations in the framework of AS-ALD. The goal was to extract molecular design descriptors which determine the deposition selectivity. It needs to be seen if more detailed studies support these preliminary findings. Since the molecular reactivity is hard to correlate to AS-ALD selectivity, most screening approaches focus on the surface chemistry of the precursors which will thus be treated in section 3.3.1.