Experimentally synthesizing predicted materials in a reproducible manner remains a key bottleneck in materials science progress. Autonomous synthesis and closed loop integration of prediction and characterization can address these issues, however, this requires autonomous characterization methods for all analysis including crystallographic phase identification which currently remains a rate-limiting step. Here we benchmark several machine learning techniques for X-ray Diffraction spectra interpretation (spectral clustering, convolutional neural networks, and invertible neural networks) and compare the relative strengths and weaknesses of each approach. Future work will involve deploying these techniques across the entire high-throughput experimental materials database.