Pioneered by Prof. Jens Nørskov and co-workers in their early works, descriptor-based analyses have now become an indispensable tool used by researchers within the heteroge-1 neous catalysis community. This philosophy is based on the hypothesis that appropriately identified descriptors, which are often binding energies of key intermediates, can help in rationalizing experimentally observed trends across catalyst compositions, thereby accelerating the design of novel functional materials for the target reaction. The continuing impact of Prof. Nørskov's contributions in this field is evidenced by several recent reviews on these topics. A defining characteristic of this Nørskovian-philosophy is the ability to describe the complexity of multistep reactions, often involving several surface-bound intermediates, using just one or two adsorption-based descriptors. Historically, this simplification was necessary to alleviate the computational costs of calculating hundreds of reaction barriers for every elementary step, and then repeating this process for several different catalyst compositions. Indeed, the concept of linear scaling relationships, which is based on the well-known bond order conservation principle, now forms the core vocabulary used within this field. Although the calculation of reaction barriers remains cumbersome, recent work by us and others has demonstrated how machine learning potentials (MLPs) can be used to overcome this bottleneck. For instance, using Cu-exchanged zeolites as a prototypical example, we have explicitly calculated the transition state geometries and reaction barriers of methane activation for thousands of [CuOCu] 2+ sites across 52 zeolites. While most sites show linear trends between the C-H activation barrier and the H binding energy, our analysis identifies several important factors (e.g., confinement and accessibility) that cause deviation from the expected universal scaling behaviour. Similarly, building upon the foundation provided by scaling relationships, other groups have proposed novel strategies to further improve the accuracy of these approximations. Analogous to how the development of MLPs has enabled the calculation of reaction barriers (which are usually intractable with density functional theory (DFT)), in this study, we focus on the related but less explored phenomena of surface diffusion. Indeed, surface phenomena such as adsorption, diffusion, and desorption of molecular and atomic adsorbates are of wide interest in the field of catalysis. Several studies have shown that slow diffusion processes can have a significant impact on the overall rate of catalytic reactions. While the typical approach within the field relies on using nudged elastic band calculations and harmonic transition state theory (hTST) to estimate diffusion barriers, here, we propose an alternative strategy that uses sufficiently long, MLP-based molecular dynamics simulations to measure adsorbate diffusivities without making any prior assumptions. Specifically, we calculate temperature-dependent diffusivities of key adsorbates using Ag(111) as an example of an industrially relevant catalyst. We use a learning curriculum that combines ab-initio molecular dynamics (AIMD) simulations of gas-phase species along with AIMD simulation of the bound adsorbates to obtain a transferable DeepMD-Kit-based model. Importantly, we also show that the resulting model is easily transferable to related species that were not included in the original training dataset. Taken together, this study highlights the wide versatility and growing relevance of MLP-based approaches in investigating phenomena that are typically not possible using traditional DFT simulations.