Considering machine-learning forcefields (MLFFs), which are a notable success from the application of data-driven approaches in materials modelling, a current challenge is to build these approaches to reproduce experiment, and not just higher-level computational models. Further extension of the MLFFs should then include multiple compositional and environmental aspects of a fully operational heterogeneous catalyst; and for this more efficient modelling paradigms are needed to create bigger datasets. Future challenges then arise with the integration of computational and experimental datasets, whereby parameters and observables from each respective domain must be collated and compared on an equal footing to provide value to the researchers of the future.