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English(EN) FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions

新AI方法在数据稀疏的情况下改进材料属性预测 · 跟踪4个来源

作者 PulseAugur 编辑部 · [4 个来源] ·

研究人员开发了新的材料属性预测方法，特别是在数据有限的情况下。FrOGS是一种离散神经采样器，它采用混合方法，结合了自回归模型和连续时间马尔可夫链，以准确估算各种化学条件下的热力学性质。此外，DISTAL和SemiMat框架解决了与结构无关的材料属性预测问题。DISTAL利用了自监督的成分预训练和来自结构感知模型的知识蒸馏，而SemiMat则引入了一个基准和一种称为MatRank的可靠性加权目标，用于半监督回归，两者都旨在在结构或标记数据稀缺时改进预测。 AI

影响 这些进展提供了更强大、更有效的方法来预测材料属性，尤其是在数据稀缺的环境中，有可能加速材料的发现。

排序理由 该集群包含多篇学术论文，详细介绍了材料属性预测的新方法和基准。

在 arXiv cs.LG 阅读 →

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新AI方法在数据稀疏的情况下改进材料属性预测 · 跟踪4个来源

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该集群包含多篇学术论文，详细介绍了材料属性预测的新方法和基准。
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报道来源 [4]

  1. arXiv cs.LG TIER_1 English(EN) · Kyucheol Min, Elyssa Hofgard, Tess Smidt ·

    FrOGS: 用于独立合金配置跨化学条件的离散神经采样器

    arXiv:2609.02948v1 Announce Type: cross Abstract: Predicting the thermodynamic properties of an alloy requires sampling its configurations across many chemical conditions and recovering free energies on a common absolute scale. Markov chain Monte Carlo (MCMC) is the standard tool…

  2. arXiv cs.AI TIER_1 English(EN) · Weiran Wang, Xintong Huo, Yueying Wang, Yusi Fan, Wenyan Wang, Xin Feng, Ruihao Xin, Lan Huang, Kewei Li, Fengfeng Zhou ·

    DISTAL：用于结构无关材料属性预测的蒸馏和自监督预训练

    arXiv:2609.00059v1 Announce Type: cross Abstract: Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled samples. Models with the strongest predictive accuracy often depend on crystal st…

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

    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 …

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    从稀疏标签和无标签晶体中学习材料性质

    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 cont…

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