The vast majority of computations apply static DFT computations. However, several aspects of AS-ALD research would highly benefit from dynamic modeling. For example, the penetration of an inhibitor layer by a precursor is certainly not a static chemical reaction but will be highly influenced by the dynamic response of the layer to the incoming chemical species. Or the selectivity of inhibitor adsorption on GS and NGS, which is not only determined by kinetics and thermodynamics but would require modeling of adsorption probability for a full picturesimilar to the experimental evaluation of sticking coefficients. The main reason that hardly any such studies are available is the prohibitive computational demand for the required AIMD methods to simulate long enough time scales (section 2.4). The development of machine-learned potentials could be a remedy here, enabling much more efficient computations. Another option could be the usage of the upcoming efficient yet accurate semiempirical approaches like GFN2-xTB.