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.
Crystal structure prediction (CSP) from powder diffraction data is a central challenge in materials chemistry. Machine learning (ML) models show promise, but most are trained on idealized simulated data, limiting reliability on real experiments. Here, we assess real-world behaviour using the previously published deCIFe...
Frederik L. Johansen, A. Sapnik, Erik B. Dam et al.· Digital Discovery· 0 citations
High-throughput experimentation and self-driving laboratories are drastically accelerating materials discovery, yet automated interpretation of X-ray powder diffraction (XRPD) data remains a critical rate-limiting step. Conventional search-match workflows rely heavily on expert manual intervention, while pure data-driv...
Hongqing V. Wang, Ming-Wei Chen, Hong Luo et al.· 0 citations
High-Energy Diffraction Microscopy (HEDM) recovers crystallographic orientation, strain, and grain position from rotating-crystal X-ray diffraction patterns. Existing forward models in far-field (FF), near-field (NF), and point-focused (pf) HEDM are not differentiable, which forecloses gradient-based joint parameter re...
Hemant Sharma, Nina Andrejevic, Simon Zhang et al.· 1 citation
A novel approach to post mortem characterisation of electrochemical and photovoltaic devices is spatially-resolved diffraction using a hyper-focused, micron-width x-ray beam to examine the distribution of degradation products and strain, a technique called $\mu$-XRD. Aside from the experimental difficulties associated...
L. Nagle-Cocco, C. Crain, Michael J. Dzara et al.· 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
We introduce a data-efficient neural-vision assisted method to automate crystallographic alignment during molecular beam epitaxy (MBE) growth. Trained on reflection high-energy electron diffraction (RHEED) patterns from only 15 CdTe structures, our model - enabled by physics-aware postprocessing - reliably infers cryst...
Bartłomiej Turowski, Jakub Meixner, Róża Dziewiątkowska et al.· 0 citations
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