Ab initio data can also be used to feed multiscale models via the connection of kMC and MD simulations (Fig. .). This means that reaction thermodynamics and barriers can be fed into largerscale methods. As outlined in section 2.6, this is far from trivial. Up to now, there is only one study combining DFT, MD, kMC and CFD approaches for AS-ALD modelling. Typically, DFT results are fed into a stochastic model for coverage modeling. This is mostly done in a qualitative fashion, i.e. taking the most stable product or the vdW volume of the inhibitor from DFT modeling for the stochastic modeling. An approach that has only been applied once in AS-ALD modeling is the actual usage of reaction barriers from DFT computations to feed a kMC model. This has been shown for ALD growth modelling to give insights into the atomistic processes. In principle, machine learning approaches are also able to make the direct connection between ab initio modelling and large-scale properties, e.g., via learning from data (structures, energies, etc.)