Out of 17 nanoparticles, 16 were correctly classified (Fig. ). Nanoparticles 6, 9, and 14 are the same as nanoparticles 5, 8, and 13 at different magnifications, respectively, and they maintain the expected classification trends. The one wrong prediction (trigonal as opposed to cubic, particle 17 in Fig. and SI Fig. ) is based on the only single crystalline nanoparticle, for which all DPs are from the same zone axis, which makes a unique classification impossible. In this particular case, the particle's orientation is near the cubic <101> zone axes, which exhibit the same symmetry as the trigonal <101>. Accordingly, the correct opinion -"cubic" -is classified as the second probable class for that nanoparticle. Cases like these are inevitable when analyzing individual nanoparticles and highlight the power of our MVOF-ML framework to accumulate data. In a high-throughput experimental data acquisition process, the framework would be allowed to accumulate data across particles to arrive at a "global opinion" for a particle array, eliminating the possibility of incorrect classification due to an insufficient number of crystal orientations being acquired. In addition, cases where there are multiple predictions with comparable probabilities could not only be resolved in a similar fashion, but the number of iterations it takes for the framework to arrive at its final opinion could serve as a metric of difficulty for classification, pinpointing structures of particular interest for manual examination. These results show that the high accuracy observed on simulated data is maintained on noisy experimental data, which underscores the importance of designing robust systems for use in an experimental context and highlights the effectiveness of this MVOF-ML framework.