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English(EN) Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Meta FAIR 发布大型无机材料数据集和人工智能模型

作者 PulseAugur 编辑部 · [1 个来源] ·

Meta FAIR 发布了 Open Materials 2024 (OMat24) 数据集，该数据集包含超过 1.1 亿个无机材料的密度泛函理论计算。此次发布还包括配套的预训练 EquiformerV2 模型，这些模型在 Matbench Discovery 排行榜上取得了最先进的性能。这些模型能够高精度地预测基态稳定性和形成能，旨在加速人工智能驱动的材料发现。 AI

影响 通过提供大型开放数据集和高性能模型来预测材料特性，从而加速人工智能驱动的材料发现。

排序理由 该集群描述了在 arXiv 上发布的用于材料科学研究的新数据集和配套人工智能模型的发布。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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Meta FAIR 发布大型无机材料数据集和人工智能模型

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该集群描述了在 arXiv 上发布的用于材料科学研究的新数据集和配套人工智能模型的发布。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Luis Barroso-Luque, Muhammed Shuaibi, Xiang Fu, Brandon M. Wood, Misko Dzamba, Meng Gao, Ammar Rizvi, C. Lawrence Zitnick, Zachary W. Ulissi ·

    Open Materials 2024 (OMat24) 无机材料数据集和模型

    arXiv:2410.12771v2 Announce Type: replace-cross Abstract: The ability to discover new materials with desirable properties is critical for numerous applications from helping mitigate climate change to advances in next generation computing hardware. AI has the potential to accelera…

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