SeriesXRD: automated reflection tracking and phase identification in high-pressure X-ray diffraction series
Abstract
High-pressure synchrotron X-ray diffraction increasingly produces hundreds to thousands of detector frames in a single experiment, while separating, assigning, and tracking weak reflections across an entire series often remain manual. This bottleneck is particularly severe for micron- and nanoscale samples in diamond-anvil cells, where scattering from the anvils, gasket, pressure medium, and pressure markers can dominate the sample signal. We present SeriesXRD, an open-source workflow that analyzes diffraction as a series rather than as independent patterns. The program suppresses azimuthally localized diamond scattering, subtracts background, fits and tracks reflections between frames, matches known phases using pressure-dependent lattice parameters, subtracts confidently assigned contributions, and groups persistent unexplained reflections for follow-up as candidate sample-associated or unknown-phase features. We demonstrate the method on 1288 frames collected from an approximately 30-nm-thick layered UOTe flake in a diamond-anvil cell over 3.5-50.7 GPa. The dataset is strongly background dominated, with detectable UOTe diffraction in only approximately 4% of the frames. SeriesXRD recovered the sample-associated reflections identified in the original manual analysis while reducing the corresponding phase-assignment and residual-analysis workflow from more than two months of researcher time to an overnight automated run. Across the full series, 17,143 peaks were fitted, 14,395 passed quality criteria, and 90% of accepted peaks were assigned to known background or reference phases. SeriesXRD therefore converts large, background-dominated diffraction series into a tractable set of phase assignments and coherent unexplained reflections while retaining a traceable record of each decision.
Related
- Tracking the Emergence and Persistence of Abnormal Grain Growth in an Aluminum Alloy using Diffraction-Contrast Tomography
- Scalable machine learning framework for multiphase identification from powder X-ray diffraction
- Spectroscopy With Intelligent Feature Tracking for automated characterization of excited-state structure in semiconductor quantum dot devices
- VoroTypes: automated enumeration of Voronoi cell topologies for structural identification of complex crystals in atomistic simulations
- FPGA-based muon shower identification and graph neural network tracking algorithms for HL-LHC triggers
BibTeX
@misc{maples2026seriesxrd,
title = {{SeriesXRD: automated reflection tracking and phase identification in high-pressure X-ray diffraction series}},
author = {Rush Maples and Zackary Rehfuss and Hongyi Liu and Sheng Ran},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.00611},
eprint = {2611.00611},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.00611},
}
RIS
TY - UNPB
TI - SeriesXRD: automated reflection tracking and phase identification in high-pressure X-ray diffraction series
AU - Rush Maples
AU - Zackary Rehfuss
AU - Hongyi Liu
AU - Sheng Ran
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.00611
UR - https://arxiv.org/abs/2611.00611
AB - High-pressure synchrotron X-ray diffraction increasingly produces hundreds to thousands of detector frames in a single experiment, while separating, assigning, and tracking weak reflections across an entire series often remain manual. This bottleneck is particularly severe for micron- and nanoscale samples in diamond-anvil cells, where scattering from the anvils, gasket, pressure medium, and pressure markers can dominate the sample signal. We present SeriesXRD, an open-source workflow that analyzes diffraction as a series rather than as independent patterns. The program suppresses azimuthally localized diamond scattering, subtracts background, fits and tracks reflections between frames, matches known phases using pressure-dependent lattice parameters, subtracts confidently assigned contributions, and groups persistent unexplained reflections for follow-up as candidate sample-associated or unknown-phase features. We demonstrate the method on 1288 frames collected from an approximately 30-nm-thick layered UOTe flake in a diamond-anvil cell over 3.5-50.7 GPa. The dataset is strongly background dominated, with detectable UOTe diffraction in only approximately 4% of the frames. SeriesXRD recovered the sample-associated reflections identified in the original manual analysis while reducing the corresponding phase-assignment and residual-analysis workflow from more than two months of researcher time to an overnight automated run. Across the full series, 17,143 peaks were fitted, 14,395 passed quality criteria, and 90% of accepted peaks were assigned to known background or reference phases. SeriesXRD therefore converts large, background-dominated diffraction series into a tractable set of phase assignments and coherent unexplained reflections while retaining a traceable record of each decision.
ER -