Differentiable powder X-ray diffraction enables analysis of evolving structures
Abstract
Operando and in-situ experiments extend powder X-ray diffraction (PXRD) to materials that evolve during experiments, producing series of hundreds or thousands of patterns. Such series are commonly refined one pattern at a time, so weak signals give noisy structural trajectories. Parametric refinement instead fits a shared model to all patterns, but with most existing programs, changing the model or fitting it efficiently remains challenging. Here, we introduce diffPXRD, a differentiable PXRD engine in PyTorch. diffPXRD calculates derivatives by automatic differentiation, so a shared model can be changed without new derivative code. Batched fitting is also more than seven times faster than serial fitting. In an operando cathode series, a shared lattice model can reduce lattice-parameter variability by 68.4% when 10% of the photon counts are retained. We also show shared models can describe the precipitation and dissolution of Li2S and reconstruct collective atomic displacements in Ca3Ti2O7.
Related
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BibTeX
@misc{yin2026differentiable,
title = {{Differentiable powder X-ray diffraction enables analysis of evolving structures}},
author = {Xiangyu Yin and Nina Andrejevic and Hemant Sharma and Ming Du and Aileen Luo and Michael Prince and Burak Guzelturk and Xiaoyi Zhang and Brian H. Toby and Mathew J. Cherukara},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.28564},
eprint = {2610.28564},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.28564},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Differentiable powder X-ray diffraction enables analysis of evolving structures
AU - Xiangyu Yin
AU - Nina Andrejevic
AU - Hemant Sharma
AU - Ming Du
AU - Aileen Luo
AU - Michael Prince
AU - Burak Guzelturk
AU - Xiaoyi Zhang
AU - Brian H. Toby
AU - Mathew J. Cherukara
PY - 2026
DA - 2026/10/20
PB - arXiv
DO - 10.48550/arxiv.2610.28564
UR - https://arxiv.org/abs/2610.28564
AB - Operando and in-situ experiments extend powder X-ray diffraction (PXRD) to materials that evolve during experiments, producing series of hundreds or thousands of patterns. Such series are commonly refined one pattern at a time, so weak signals give noisy structural trajectories. Parametric refinement instead fits a shared model to all patterns, but with most existing programs, changing the model or fitting it efficiently remains challenging. Here, we introduce diffPXRD, a differentiable PXRD engine in PyTorch. diffPXRD calculates derivatives by automatic differentiation, so a shared model can be changed without new derivative code. Batched fitting is also more than seven times faster than serial fitting. In an operando cathode series, a shared lattice model can reduce lattice-parameter variability by 68.4% when 10% of the photon counts are retained. We also show shared models can describe the precipitation and dissolution of Li2S and reconstruct collective atomic displacements in Ca3Ti2O7.
ER -