PXtal: Learning to Align Powder X-Ray Diffraction and Crystal Structures under Information Asymmetry across Modalities

Key points

• Scientific multimodal learning commonly assumes that paired views are comparably informative.1
• Powder X-ray diffraction (PXRD) makes this mismatch explicit: compressing a three-dimensional crystal structure into a one-dimensional diffraction pattern loses information and makes the pattern harder to connect to the crystal structure that produced it.1
• Across six test sets, including four zero-shot transfer sets, PXtal consistently outperforms the baseline models in PXRD-to-crystal candidate retrieval, with the largest gains when PXRD patterns have close but crystallographically distinct nonpaired neighbors, meaning similar input patterns associated with different crystals.1
• The resulting crystal and PXRD encoders transfer more effectively to downstream materials and crystallographic tasks.1

Sources (1)

• [1]PXtal: Learning to Align Powder X-Ray Diffraction and Crystal Structures under Information Asymmetry across Modalities
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 03:59 PM

  We introduce PXtal, a framework for learning aligned PXRD and crystal representations under this physically imposed information asymmetry.

  Scientific multimodal learning commonly assumes that paired views are comparably informative.

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