Mean-field kinetic modelling have been used to investigate the evolution of platinum (Pt) nanoparticles on , including effects such as atom capture, Oswald ripening, and particle coalescence. Simulation of the morphology evolution using these methods have given results in good agreement with experimental measurements. Although giving high-level insight, these methods do not have atomistic resolution as they rely on mean field methods, hindering the possibility of finding atomistic descriptors for morphology prediction. In our previous work on the development of a combined barrier/layer material we instead used atomistic modelling using density functional theory (DFT) and ab-initio molecular dynamic simulation (aiMD) to investigate the morphology preference of Cu on pure and doped TaN films. The use of DFT allowed us to gain atomistic resolution and identify properties such as the metal-to-metal clustering energy and metal-to-surface binding energy which could be used to predict the morphology preference. In this study we continue developing the approach of identifying atomistic predictors for metals on TaN by predicting the morphology evolution of Co and Ru metal on TaN. We also simulate the morphology evolution over a longer timescale to elucidate the effect of time on the clusters. This allows us to expand our modelling framework to encompass more metals and compare their different properties, leading towards the continuing development of new interconnect materials.