A common use-case of unsupervised methods is the analysis of structural data with varying chemistry to construct experimental phase diagrams (Fig 9a . DTW finds the optimal shift and warp-corrected correlation between data points, making it efficient at recognizing shifted XRD patterns but unable to assess peak broadening. Other solutions to peak shifting include the use of a logarithm kernel to the angle/q axis to transform increasing peak shifts at higher angles into a constant shift factor across the transformed pattern The existing literature on the ML analysis of X-ray scattering and diffraction offers a strong basis for future expansions and applications in autonomous and high-throughput experimentation.