The hyperparameters and the architecture of the autoencoder are also optimized by maximizing the F1 score over multiple folds. Figure shows that the model labels 8 out of 15 auxetic as Anomalies, while only 5 out of 1119 non-auxetic are incorrectly labeled as Anomalies. The threshold for labeling anomalies is approximately 99 percentile of the total reconstruction error for the testing case. The model successfully labels all the α-cristobalite SiO 2 materials as anomalies, which are well known auxetics. 32 Additionally, we make separate sets of materials with high reconstruction error(above 90 percentile) and low reconstruction. This is done to signify the features that are being reconstructed poorly in the materials that are being labeled as potential anomalies. If a property is reconstructed properly, both the histogram should be overlapped, if not, there should be a clear separation between the two sets. We found that average d-valence electrons and average density are not reconstructed properly for the high reconstruction error set (Figure . 3). Many features are reconstructed well throughout the whole region(Figure ). Interestingly, it is well known that density and valence electrons are important properties for predicting Poisson's ratio (Figure ) Moreover these quantities have good correlation with the Poisson's ratio as shown in Figure . This further validates that the autoencoder is learning the pattern of positive Poisson's ratio.