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English(EN) Complex materials engineering via Provenance Action Graphs: potential, structure and demonstration

AI 驱动的图加速复杂材料发现

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员探索了使用 Provenance Action Graphs (PAGs) 来推进材料工程，特别是用于合成复杂和动力学稳定的材料。这些图将合成事件表示为反应步骤及其结果，旨在捕捉成功的配方以及大量通常未发表的失败案例。通过利用 AI 算法，目标是解码合成路径，以加速材料发现并为数据代理创建共享的全球知识库。 AI

影响 通过 AI 驱动的知识库，有潜力显著加速材料发现和合成。

排序理由 详细介绍材料工程新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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AI 驱动的图加速复杂材料发现

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16 / 100
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详细介绍材料工程新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yevgeny Rakita, Paul Todd, James R. Neilson, Simon J. L. Billinge ·

    通过 Provenance Action Graphs 实现复杂材料工程：潜力、结构与演示

    arXiv:2610.18314v1 Announce Type: new Abstract: This work discusses and tests the potential of reaction graphs for material synthesis, in which events consist of reaction steps (actions) and their outputs (observations). The importance comes from the requirement to synthesize tec…

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