Learning Materials Properties from Scarce Labels and Unlabeled Crystals

2026-08-31

AI Summary

本文通过提出SemiMat基准和MatRank方法，解决了从少量标记和未标记晶体中学习材料属性的问题，实现了在有限标签条件下对材料性质的有效预测。

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.

Problem

• scarce labels
• unlabeled crystals
• materials properties
• data-driven materials discovery

Innovation

• semi-supervised learning
• materials property regression
• reliability-weighted objective
• pseudo-label uncertainty
• graph backbones