KAN-Enhanced Contrastive Learning Accelerating Crystal Structure Identification from XRD Patterns

Accurate determination of crystal structures is central to materials science, underpinning the understanding of composition–structure–property relationships and the discovery of new materials. Powder X-ray diffraction (XRD) is a key technique in this pursuit due to its versatility and reliability, yet current analysis pipelines still rely heavily on expert knowledge and slow iterative fitting, limiting their scalability in high-throughput and autonomous settings. Here we introduce a physics-guided contrastive learning framework, XRD–Crystal Contrastive Pretraining (XCCP), which aligns powder diffraction patterns with candidate crystal structures in a shared embedding space to enable efficient structure retrieval and symmetry recognition. The XRD encoder employs a dual-expert design with a Kolmogorov–Arnold Network projection head: one branch emphasizes low-angle reflections reflecting long-range order, while the other captures dense high-angle peaks shaped by symmetry. Coupled with a crystal graph encoder, contrastive pretraining yields physically grounded representations. XCCP demonstrates strong performance across tasks, with structure retrieval reaching 88.98% and space group identification attains 93.39% accuracy. The framework further generalizes to compositionally similar multi-principal element alloys and demonstrates zero-shot transfer to experimental patterns. Together, these results establish XCCP as a robust, interpretable, and scalable approach that offers a new paradigm for PXRD analysis, facilitating high-throughput screening, rapid structural validation, and integration into autonomous laboratories.