Author

Xiean Wang

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Open access Aug 2026

MatUQ: a benchmark for uncertainty-aware out-of-distribution materials property prediction with graph neural networks

This work introduces MatUQ, a benchmark built on structure-aware Smooth Overlap of Atomic Positions Leave-One-Cluster-Out (SOAP-LOCO) splitting, together with a training protocol that combines Deep Evidential Regression (DER) with dropout regularization, for evaluating GNN reliability under structural distribution shifts.

Liqin Tan, Xiean Wang, Yuexin Zou et al. · npj Computational Materials · 0 citations