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English(EN) XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models

扩散模型从X射线衍射数据预测晶体结构

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了XRDiff，这是一种能够从粉末X射线衍射（PXRD）数据预测晶体结构的新型扩散模型。该模型可以根据已知的化学计量比或更具挑战性的元素组成和晶胞原子数来推断结构。XRDiff通过基于峰值的编码在区分多晶型物方面表现出强大的性能，该编码对噪声和伪影具有鲁棒性，并有望推广到实验数据。 AI

影响 这项研究通过实现从实验数据中更快、更准确地确定晶体结构，有望加速材料发现。

排序理由 该集群包含一篇详细介绍用于科学应用的新AI模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

扩散模型从X射线衍射数据预测晶体结构

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该集群包含一篇详细介绍用于科学应用的新AI模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Nofit Segal, Mingda Li, Benjamin Kurt Miller, Rafael G\'omez-Bombarelli ·

    XRDiff：使用扩散模型从粉末X射线衍射数据进行晶体结构预测

    arXiv:2606.14003v1 Announce Type: cross Abstract: Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science. PXRD is an accessible and widely used characterization technique, yet recovering the ato…

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