"There is plenty of room at the bottom." The famous statement by Feynman emphasizing the not yet realized potential of nanotechnology is also true for the ongoing downscaling of devices in microelectronics. While the gate length in transistors was 50 µm in the mid-1960s, the smallest features in currently produced chips are approaching 1 nm. The result is requiring an unprecedented accuracy in fabrication which still mostly relies on "top-down" techniques like etching, using elaborate and very expensive setups. This creates the possibility for "bottom-up" techniques to become competitive. One of the major approaches here is atomic layer deposition (ALD) which uses self-limited surface chemistry to produce thin films with precise thickness. Here, a key feature of ALD is the separation of the reactive species in the gas phase which is achieved by pulsing only one reactive species in the reaction chamber at a time (Fig. .a). Once all species are added, one ALD cycle is completed, while repetition of ALD cycles leads to material deposition. Over the last decades, efficient ALD processes have been developed for a large range of materials on a diverse set of substrates. Increasingly, the demand is not only for well-defined thicknesses but also for selectivity in deposition. This results from the requirement to deposit material on one growth surface (GS) while a neighboring non-growth surface (NGS) is left clean (Fig. 1.d). One key application for this areaselective ALD (AS-ALD) process is the fully self-aligned via (FSAV) -a key component in microelectronic circuits (Fig. ). Ideally a via connects two metal lines in different layers of the microelectronic circuit while being perfectly aligned to the top and bottom line as shown in Fig. . However, creating vias with top-down approaches can lead to edge-placement errors (Fig. .b). These errors can promote electron tunneling resulting in a loss of device functionality. As a solution, edge-placement errors can be prevented by the high deposition accuracy of AS-ALD processes and the introduction of FSAVs (Fig. ). In 2021, TSMC demonstrated the use of AS-ALD to achieve such a via with the goal of interconnect expansion beyond 3 nm nodes. As a different target application, the possibility to deposit material in complex 3D structures is providing opportunities for AS-ALD, e.g., in modern memory devices. Beyond microelectronic technology, (AS-)ALD is used in fields like catalysis, solar cells or battery research. Furthermore, AS-ALD has been combined with top-down patterning approaches, e.g. in the case of TiO2 where the selective deposition is used for tone inversion and resist hardening. The major challenge in AS-ALD is achieving (near-)perfect selectivity for a high number of ALD cycles. Usually, this is determined by the ratio of material deposited on the GS in comparison to the material deposited on the NGS (Fig. .g). A more detailed discussion is found in section 0. This goal can be achieved through a variety of routes which can be categorized as: (i) intrinsically selective (Fig. .d), (ii) area-activating (Fig. .e) and (iii) area-deactivating (or inhibitor-based, Fig. .f) approaches. All these approaches rely on an in-depth understanding of deposition and surface chemistry to analyze and improve the selectivity for a given process. For example, the precursor can chemically modify the inhibitor layer and thus lead to growth on the NGS. Or the co-reactant modifies the NGS to make it susceptible for the precursor in the next ALD step. Therefore, the chemical interactions between all components are crucial for achieving selectivity: precursor, co-reactant, surface and inhibitor or activator. Fig. . Schematic illustration of the AS-ALD process. This can be done via an AB-type ALD process (a) without or (b) with an initial inhibitor deposition or (c) an ABC-type process including inhibitor redosing. Selectivity can be obtained based on (d) intrinsic selectivity, (e) activation of the growth surface, (GS) or (f) deactivation of the non-growth surface (NGS). Here, selectivity is defined as (g) the relative deposition on GS in contrast to the NGS. Thus, a microscopic understanding of the chemical interactions in the system is crucial for optimization of the AS-ALD process and achieving the best possible selectivity. This is where computational approaches play a key role. Computational chemistry as a research field uses methods from theoretical chemistry to solve research questions in the chemical sciences. While a more detailed discussion of the methods used in AS-ALD modelling is presented in section 0, a brief overview is provided as orientation for the reader at this point supported by Fig. . Computational methods can be categorized in static and dynamic approaches. Static approaches solve the underlying equations for one point in time while dynamic approaches describe the development of a system over a given period. Another distinction concerns the description of the interaction between the fundamental components of the chemical system (nuclei and electrons). This can either be a description using classical mechanics (using Newton's equations of motion) or quantum mechanics (using the Schrödinger equation). The quantum mechanical approaches describe electrons explicitly and typical examples are density functional theory (DFT) and wavefunction-based methods (e.g., Hartree-Fock (HF)). Molecular dynamics (MD) approaches can then either use classical potentials to describe the interactions of nuclei (classical MD) or quantum-mechanical potentials (ab initio or AIMD). More recently, machine learning (ML) has been used as a new paradigm to generate interaction potentials (MLP). Approaches spanning more than one regime of time scales and system sizes shown in Fig. . are termed multiscale modelling. While MD approaches can tackle nanosecond timescales at best, real ALD processes take place on timescales of seconds or minutes. To capture this theoretically, the above-mentioned approaches are not suitable. Here, methods for solving sets of rate equations can be used. Most prominent in ALD is the stochastic approach of using a Monte Carlo (MC) ansatz to solve kinetic rate equations (kinetic MC or kMC). This approach can then use input from quantum mechanical approaches (reaction barriers and energies) which effectively makes it a multiscale (more precisely two-scale) approach. If no information about barriers is available, a stochastic approach of surface adsorption can be applied which uses information like van-der-Waals volume of adsorbates and surface reactive sites to provide the change in surface coverage during the ALD process. These MC approaches are currently in increasing usage in AS-ALD modelling. AS-ALD is experimentally carried out in a reactor which is mostly neglected in the modelling. Although the gas phase chemistry is much less important in ALD compared to chemical vapor deposition (CVD) and similar approaches, the flow mechanics can be important, e.g., for fast surface reactions which are diffusion-limited. Modelling these phenomena requires approaches that do not rely on the chemical structure of the system anymore but instead solve coupled rate equations, like computational fluid dynamics (CFD) or finite-element (FE) methods. The coupling of such large-scale approaches to kMC, AIMD or DFT in a multiscale ansatz has been a longstanding goal (not only) of the ALD community and some progress is visible. Fig. . Overview of modelling approaches used for AS-ALD divided into static and dynamic calculations and sorted by typical time scales and system sizes. Accuracy increases to the left.