Learning Materials Properties from Scarce Labels and Unlabeled Crystals

Abstract:Learning materials properties from scarce labels and unlabeled crystals is a central challenge for data-driven materials discovery. We present SemiMat, a controlled benchmark for semi-supervised materials property regression, and MatRank, a reliability-weighted objective for continuous pseudo-label uncertainty. SemiMat fixes labeled and unlabeled crystal inputs, graph-backbone interfaces, validation-only checkpoint selection, held-out test reporting, normalized MAE (NMAE), and method-rank summaries across six scarce-label tasks, four graph backbones, and five predefined split runs. MatRank builds pseudo-targets from labeled anchors, weights them by local reliability and weak-prediction agreement, trains weak and strong graph views consistently, and adds ranking signals so that unlabeled crystals shape both values and candidate order. Across the retained 24 backbone-task blocks, one fixed MatRank objective gives the lowest aggregate held-out test NMAE (0.896) and best average method rank (2.208). The component, OOD, and generated-pool diagnostics identify where the gain is reliable and where further screening evaluation remains necessary. Code is available at this https URL.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2608.30682 [cs.LG]

(or arXiv:2608.30682v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.30682 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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