PXtal：在跨模态信息不对称下学习对齐粉末X射线衍射与晶体结构
PXtal: Learning to Align Powder X-Ray Diffraction and Crystal Structures under Information Asymmetry across Modalities
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中文总结 AI 辅助
研究针对科学多模态学习中PXRD与晶体结构的信息不对称问题，提出PXtal框架，利用UOT和GKL散度实现跨模态对齐，在六组测试集上提升了PXRD到晶体的检索性能，编码器可有效迁移至下游任务。
中文摘要 AI 辅助
科学多模态学习通常假设配对视图的信息丰富度相当，而粉末X射线衍射（PXRD）明确了这种不匹配：将三维晶体结构压缩为一维衍射模式会丢失信息，使得该模式难以与产生它的晶体结构关联。我们提出PXtal，这是一个在这种物理强制的信息不对称下学习对齐PXRD和晶体表示的框架。PXtal使用不平衡最优传输（UOT）来适配跨模态耦合，并利用耦合级广义Kullback-Leibler（GKL）散度来监督完整的传输计划。在六个测试集（包括四个零样本迁移集）上，PXtal在PXRD到晶体候选检索任务中始终优于基线模型，当PXRD模式具有晶体学上不同的近邻（即与不同晶体关联的相似输入模式）时，增益最大。由此得到的晶体和PXRD编码器能更有效地迁移到下游材料和晶体学任务。这些结果表明，信息不对称是科学多模态学习中的一个普遍设计问题：对齐目标应反映每个模态所保留的内容。
英文摘要
Scientific multimodal learning commonly assumes that paired views are comparably informative. 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. We introduce PXtal, a framework for learning aligned PXRD and crystal representations under this physically imposed information asymmetry. PXtal uses Unbalanced Optimal Transport (UOT) to adapt the cross-modal coupling and coupling-level generalized Kullback-Leibler (GKL) divergence to supervise the full transport plan. 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. The resulting crystal and PXRD encoders transfer more effectively to downstream materials and crystallographic tasks. These results identify information asymmetry as a general design problem in scientific multimodal learning: alignment objectives should reflect what each modality preserves.
发表机构
- University of Liverpool（利物浦大学）
- University of Sheffield（谢菲尔德大学）
- Leverhulme Research Centre for Functional Materials Design（勒沃休姆功能材料设计研究中心）
机构由 AI 辅助整理，请以论文原文为准。
