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[2602.16372] AI-Driven Structure Refinement of X-ray Diffraction
Summary
This paper presents WPEM, an AI-driven workflow for refining X-ray diffraction data, enhancing the stability and accuracy of peak intensity assignments in complex materials.
Why It Matters
The integration of AI in materials science, particularly in X-ray diffraction, addresses challenges in accurately determining structures from complex data. This innovation can significantly improve material characterization, impacting various fields such as chemistry, physics, and engineering.
Key Takeaways
- WPEM utilizes a physics-constrained approach to improve intensity assignment in XRD data.
- The method shows superior performance compared to existing packages like FullProf and TOPAS.
- WPEM is versatile, applicable to various experimental scenarios including multiphase materials and ancient samples.
Condensed Matter > Materials Science arXiv:2602.16372 (cond-mat) [Submitted on 18 Feb 2026] Title:AI-Driven Structure Refinement of X-ray Diffraction Authors:Bin Cao, Qian Zhang, Zhenjie Feng, Taolue Zhang, Jiaqiang Huang, Lu-Tao Weng, Tong-Yi Zhang View a PDF of the paper titled AI-Driven Structure Refinement of X-ray Diffraction, by Bin Cao and 6 other authors View PDF Abstract:Artificial intelligence can rapidly propose candidate phases and structures from X-ray diffraction (XRD), but these hypotheses often fail in downstream refinement because peak intensities cannot be stably assigned under severe overlap and diffraction consistency is enforced only weakly. Here we introduce WPEM, a physics-constrained whole-pattern decomposition and refinement workflow that turns Bragg's law into an explicit constraint within a batch expectation--maximization framework. WPEM models the full profile as a probabilistic mixture density and iteratively infers component-resolved intensities while keeping peak centres Bragg-consistent, producing a continuous, physically admissible intensity representation that remains stable in heavily overlapped regions and in the presence of mixed radiation or multiple phases. We benchmark WPEM on standard reference patterns (\ce{PbSO4} and \ce{Tb2BaCoO5}), where it yields lower $R_{\mathrm{p}}$/$R_{\mathrm{wp}}$ than widely used packages (FullProf and TOPAS) under matched refinement conditions. We further demonstrate generality across realistic experime...
