where d is depth (number of layers or convolutions ) and w is width (the embedding dimension or number of basis functions employed by each convolution). Capacity is a simple parameter to vary during neural scaling experiments, because model size is not a strictly useful scaling parameter for GNNs . Typical evaluations of NFFs consider training dataset sizes of less than 1,000 3D geometries of a single chemical species, which leads to insensitivity to model capacity because of the simplicity of the learning task .