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cond-mat.mtrl-sci2026
Measuring trainable degrees of freedom in materials graph neural networks: a random-subspace intrinsic dimension analysis
Shehroz Ahmad Shoaib, Kangming Li
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 o…
cond-mat.mtrl-sci2026
Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives
Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6
Materials science data collection can be expensive, making dataset reuse and long-term utility important for future discovery. In practice, data collection often prioritizes proper…