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<p>Although crystal parameter prediction from powder X-ray diffraction has recently attracted the interest of the machine learning community, most existing datasets for this task are private and lack structural diversity. Here, we introduce the Simulated Powder X-ray Diffraction Open Database (SIMPOD), a new dataset that is public and structurally varied. This new benchmark includes 467,861 crystal structures from the Crystallography Open Database (COD) and their powder X-ray diffraction patterns. SIMPOD presents simulated one-dimensional powder X-ray diffractograms and derived two-dimensional radial images to facilitate the adoption of computer vision models for this task. We hope SIMPOD contributes to developing models that improve materials analysis from powder X-ray diffraction.</p>

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A new benchmark for machine learning applied to powder X-ray diffraction

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月10日

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  • Sergio Rincón,
  • Gabriel González,
  • Mario A. Macías,
  • Pablo Arbeláez

摘要

Although crystal parameter prediction from powder X-ray diffraction has recently attracted the interest of the machine learning community, most existing datasets for this task are private and lack structural diversity. Here, we introduce the Simulated Powder X-ray Diffraction Open Database (SIMPOD), a new dataset that is public and structurally varied. This new benchmark includes 467,861 crystal structures from the Crystallography Open Database (COD) and their powder X-ray diffraction patterns. SIMPOD presents simulated one-dimensional powder X-ray diffractograms and derived two-dimensional radial images to facilitate the adoption of computer vision models for this task. We hope SIMPOD contributes to developing models that improve materials analysis from powder X-ray diffraction.