Differentiable powder X-ray diffraction enables analysis of evolving structures

Overview & Synopsis

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.