Hi OCP team, thank you for the dataset and codebase! I had a couple queries re. the choice of baseline models across OC20 tasks:
Did you consider any simpler baselines besides Graph Neural Networks? For e.g. some linear models specialized for such tasks, such as Moment Tensor Potentials, or some basic machine learning baselines s.a. SVMs on the input features?
Within GNNs, I noticed that you chose architectures which are specialized to OC20-like tasks. Could you comment on/did you also benchmark the performance of generic GNNs such as the basic Graph Convolution, Graph Attention, and Graph Isomorphism Nets? (As I recall that the ocp-models repository used to have classes for these architectures at some point in the past…)