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

The paper introduces a new way to evaluate materials graph neural networks (GNNs) by measuring how many trainable parameter‑space directions are needed to achieve good performance. Using random‑subspace intrinsic‑dimension analysis, the authors train CGCNN, ALIGNN, and DimeNet++ on six prediction tasks and plot recovery curves that separate final accuracy from the dimensional demand required to reach it. The study finds that different tasks and architectures vary in how sensitive they are to dimensional restriction, revealing insights that final error metrics alone miss.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.