In this work, DPs are described as vector maps containing position and intensity information of Bragg disks to encapsulate the parts of the data in a condensed format that contains physical information. We compared the predictive performance of the CNN model using vector maps versus using images as inputs. Due to the difference in types of inputs (i.e., images and tabular data), we used different CNN architectures for each input type. For grayscale image inputs, we adopted the typical CNN with several combinations of a convolutional and max pooling layer followed by the fully connected layer and the output layer. For vector map inputs, we used a modified PointNet CNN architecture, as described earlier. We tested the predictive performance given 1, 2, and 3 images or vector maps as inputs (corresponding to 1, 2, or 3 multiple DPs for each case), and we found that the accuracy is always higher when vector maps are used as DP descriptors (SI Fig. ). In addition, we again observed that the accuracy improves significantly when two or three DPs are input to the CNN compared to one DP (via supra). The results shown in this section are representative of the situation where testing data are obtained from the same zone axes as the training data, a common practice in the literature when ML is used for crystal structure classification from DPs. However, in real experiments, the zone axes of the collected crystal diffraction data may be unknown and random, especially in highthroughput workflows, including those involving nanoparticle megalibraries. Our MVOF-ML framework (Section 2.1) can avoid that problem, significantly increasing its practical application in such workflows.