PXRD-Bench: How Close Are We to Determining Crystal Structures from Powder Diffraction?
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Abstract (arXiv)
Determining crystal structures from powder X-ray diffraction (PXRD) would enable routine characterization of newly synthesized materials, including those absent from reference databases. Generative models offer a promising approach, but existing evaluations remain fragmented and largely assume single-phase samples with known composition. To fill this gap, we introduce PXRD-Bench, a unified benchmark that extends evaluation to unknown per-element atom counts and recovery of both structures in two-phase mixtures. It provides approximately 4.1 million training structures with simulated PXRD, tests generalization to unseen compositions and structural prototypes, and evaluates recovery from experimental PXRD after excluding structurally matching training entries. To provide a strong reference baseline, we also introduce Bragglite, a PXRD-to-structure model developed through a systematic study of PXRD modeling choices. It achieves a 44.2% Top-1 match rate on experimental RRUFF patterns with known composition, 24.6% above the strongest prior baseline. Further analysis yields several key findings: some models fail to benefit from experimental PXRD, while generalization to unseen structural prototypes and recovery from two-phase mixtures remain challenging. Together, our benchmark, systematic studies, and Bragglite clarify the capabilities and limitations of current PXRD-to-structure models.
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References · 8
- 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 3,045
- 2023Crystal Structure Prediction by Joint Equivariant Diffusioncited by 148
- 2026Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modelingcited by 7
- 2025AI-Driven Expansion and Application of the Alexandria Databasecited by 5
- 2025opXRD: Open Experimental Powder X-ray Diffraction Databasecited by 3
- 2026XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffractioncited by 3
- 2025The Loss Landscape of Powder X-Ray Diffraction-Based Structure Optimization Is Too Rough for Gradient Descentcited by 2
- 2026XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Modelscited by 1
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