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New benchmark SemiMat and MatRank improve AI for materials discovery

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SemiMat, a benchmark designed to evaluate semi-supervised learning for materials property regression. This benchmark addresses the challenge of learning from limited labeled data and abundant unlabeled crystal structures. Alongside SemiMat, they developed MatRank, an objective function that weights pseudo-labels based on reliability and prediction agreement to improve model performance. AI

IMPACT Enhances AI capabilities for materials discovery by improving learning from limited data.

RANK_REASON The cluster describes a new benchmark and objective function for semi-supervised learning in materials science, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New benchmark SemiMat and MatRank improve AI for materials discovery

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The cluster describes a new benchmark and objective function for semi-supervised learning in materials science, presented in an academic paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wentao Li, Yizhe Chen, Jiangjie Qiu, Yijun Li, Leyi Zhao, Xiaonan Wang ·

    Learning Materials Properties from Scarce Labels and Unlabeled Crystals

    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 …

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