In this section we test the prediction performance of the proposed methodology against state-of-the-art deep learning architectures on Materials Project and Matbench. shows a 1.89% improvement on formation energy against the second bestperforming method, whilst being approximately an order of magnitude smaller in terms of number of trainable parameters as reported in the last column.