The continuing downscaling of semiconductor devices (More Moore) and the introduction of novel device architectures and materials (More-than-Moore) requires the deposition of a range of thin films of dielectrics, metals and semiconductors on complex 3D structures with high uniformity across a wafer and high conformality, e.g. in deep trenches. This is achieved using atomic layer deposition (ALD) which utilises two precursor molecules introduced sequentially to a reactor with a purge between each step. The key property of ALD films is their self-limiting chemistry in which once a precursor saturates the surface no further reaction takes place and a cycle deposits (a fraction of) a monolayer. Thickness is controlled by the number of cycles. Molecular Layer deposition (MLD) is a sister technique that uses organic molecules to produce hybrid organic-inorganic films or polymer films using the same self-limiting chemistry. In this presentation, I will describe our work on first principles modelling of ALD and MLD of a range of materials, namely Co metal, FeZe intermetallic and hybrid organic-inorganic materials showing how the simulations can help understand a process or predict a process chemistry. In addition, I will discuss how we envisage using outputs of these large simulations to develop kinetic Monte Carlo simulations and machine learning-enabled approaches to model ALD and MLD processes and predict new chemistries.