We start by assuming an atomistic model that operates on positional inputs (distances, angles, etc.) and which computes individual representations that require aggregation into a single molecule-level embedding. The specifics of the architecture or the implementation do not matter as long as the assumptions hold. For example, many message passing neural networks can be summarised into the following generic formulation that computes node-level features h u :