Measuring trainable degrees of freedom in materials graph neural networks: a random-subspace intrinsic dimension analysis.

arXiv:2609.36084v1 Announce Type: cross Abstract: Final predictive accuracy is the standard basis for comparing graph neural networks (GNNs) in materials-property prediction, but it does not show how strongly performance depends on access to trainable paramete