Learning Materials Properties from Scarce Labels and Unlabeled Crystals

arXiv:2608.30682 · cs.LG, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/littlepeachs/SemiMat

License: http://creativecommons.org/licenses/by-nc-sa/4.0/

The gist: Learning materials properties from scarce labels and unlabeled crystals is a central challenge for data-driven materials discovery.

Terminology

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 https://github.com/littlepeachs/SemiMat.

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