English(EN) Learning Materials Properties from Scarce Labels and Unlabeled Crystals

新的基准 SemiMat 和 MatRank 改进了材料发现的 AI

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员推出了 SemiMat，这是一个旨在评估材料性质回归的半监督学习的基准。该基准解决了从有限的标记数据和大量的无标记晶体结构中学习的挑战。除了 SemiMat，他们还开发了 MatRank，这是一个根据可靠性和预测一致性对伪标签进行加权的客观函数，以提高模型性能。 AI

影响通过改进从有限数据中学习的能力，增强了材料发现的 AI 能力。

排序理由该集群在一篇学术论文中描述了一个用于材料科学半监督学习的新基准和客观函数。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Wentao Li, Yizhe Chen, Jiangjie Qiu, Yijun Li, Leyi Zhao, Xiaonan Wang · 2026-09-01 04:00

从稀疏标签和无标签晶体中学习材料性质

arXiv:2608.30682v1 Announce Type: cross 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 …