In the pursuit of global representation in graph models, increasingly more complex features are being embedded into the models in order to capture more of the vast chemical landscape. As a result the memory footprints of both the data, and the models themselves have increased drastically, making training those specialist models less feasible without dedicated high performance computing resources. Motivated by this, recent work focuses on the significantly less complex isotropic architectures , where messages are a function of the source and not the target, showing that they can efficiently capture long-range interatomic interactions and outperform in practice anisotropic models.