Another particularly challenging problem in XRD analysis is multiphase identification. Work in the 20 th century focused on the extension of Klug and Alexander's 85 equation for quantitative Xray diffraction analysis using pure and unpure standards (which should also be considered in addition to ML strategies). Starting from the 1980s, linear regression found success in phase fraction prediction which attempt to find components to represent pure phases from impure samples without standards . In recent years, CNNs have been used for multiphase fraction regression or pattern identification using simulated XRD patterns from online databases as standards. Wang et al. for the Li/Mn/Ti oxyfluoride chemical space to identify if a single phase was present in an XRD pattern. Once identified, the phase was subtracted from the pattern, and the same classification-subtraction analysis was performed until no phases were left. This work was later implemented in an autonomous phase identification diffractometer experiment as shown in Fig. 89 . Other applications of ML have been for attempts to shortcut later steps in structural analysis and extract information beyond symmetry and phase identity. For example, Chitturi et al. trained a CNN to predict lattice parameters for any XRD pattern, completing the full XRD analysis workflow from peak indexing to Rietveld refinement in a single step. However, differences between real and predicted lattice parameters exceeded 2 angstroms for some systems. Dong et al. also developed a CNN to estimate lattice parameters (along with the scale factor and crystallite size) and achieved greater accuracies by considering the Ni-Pd/CeO2-ZrO2/Al2O3 materials space rather than all materials. Lee et al. demonstrated a multi-step optimization process that differs significantly from the CNN-based methods described in earlier section. They developed an "Evolv&Morph" algorithm which uses a genetic algorithm to produce a crystal structure that closely replicate the target XRD before Rietveld refinement. ML may also find applications in linking chemistry to structure, such as the work by Dong et al. which predicted the likely XRD spectra from the elemental composition of a material in a single step for specific and well-defined chemical domains.