To test the performance of the MVOF-ML framework under realistic experimental scenarios, experimental data obtained from AuCu nanoparticle libraries are used as inputs (SI Fig. ). Data from different regions of a nanoparticle were treated as DPs from different orientations of the same crystal. In total, 17 nanoparticle datasets (SI Fig. ) with cubic crystal systems are tested. The experimental data processing for an example nanoparticle (Fig. ) is depicted (SI Fig. ). The DPs distributed at different scan positions (white squares in Fig. ) are shown (Fig. ) as well as the DPs of all the nanoparticles (SI Figs. ). At each scan position, the locations and intensities of the Bragg disks are extracted and are used as inputs in the MVOF-ML. The CNN model was retrained on the simulated data (the real experimental data are the test data) with disk intensity higher than 0.2. This intensity thresholding removes all low-intensity disks that would not be observed experimentally to provide a more realistic training dataset. The accuracy of the MVOF-ML framework on the simulated testing data was 0.80. The results for experimental data are as follows.