Powder X-ray diffraction (PXRD) is the routine probe of crystalline matter, yet its analysis is the rate-limiting step as laboratories automate acquisition. Deep-learning analyzers excel on simulated patterns and degrade on measured ones. This simulation-to-real gap is structural, not additive: synthetic denoising gives no measurable lift on real spectra, whereas correcting a small peak-position drift more than doubles median retrieval correlation. Real-spectrum fine-tuning, peak-aligned reranking, and recalibration narrow what remains and restore the coverage synthetic anchors lose. Xtalyst integrates these in an agent-orchestrated system spanning phase identification, refinement, and calibrated property prediction. On a frozen held-out partition (n=534) each module measured on both splits reproduces its development finding -- including the synthetic-anchor under-coverage, whose magnitude differs between the two pools -- while held-out refinement converges and preserves symmetry without reaching profile-quality fits, and on a diffractometer its wet-dry recommend-rescan-reanalyze loop flips a blinded silicon standard to a gated PASS and changes which minor phase is resolved on a multi-metal alloy.
Gan Jiang, a self-learning agent for powder X-ray diffraction built on a diffraction-analysis ecosystem the authors developed: XMatcher, XQueryer, XDecomposer and WPEM, demonstrates how an integrated scientific tool ecosystem can support agents that extract structural knowledge from measurements while accumulating vali...
Bin Cao, Hui-Chi Zhou, Ru-Yun Yang et al.· 0 citations
Doubly curved crystal (DCC) monochromators for X-ray fluorescence (XRF) couple crystal material, mosaicity, source/sample distances, crystal dimensions, and the meridional and sagittal bending radii, but are usually tuned empirically or one parameter at a time. A Takagi–Taupin-informed simulation system turns this coup...
Zi-Peng Wang, Qi Zhang, Mo-Hai Yue et al.· Italian National Conference...· 0 citations
GALAXI decouples the identification task into independent one-versus-all binary classifiers that each specialize in recognizing a single phase, which remains robust to common experimental artifacts, including low impurity phase fractions, small crystallite size, peak shifts, sample displacement, and texture.
Xin-Yang Tong, Ethan Jin, Jia-Hang Xu et al.· 0 citations
Atomic-scale phase imaging is central to resolving defects, interfaces, and weakly scattering atoms that govern the behavior of nanoscale materials. Electron ptychography delivers sub-{\aa}ngstr\"om phase sensitivity but remains an offline technique, because its iterative reconstruction is computationally expensive and...
H. Yue, Charles B. Chen, C.-N. Hsiao et al.· 0 citations
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 crysta...
Zhuo-Ran Yang, Christopher M. Collins, Bei Peng et al.· 0 citations
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