Although unsupervised anomaly detection techniques are widely used, it is a challenging task to connect to a targeted quantity using unlabelled data. As shown by Brogch et al the MRBT crystal features used in the study correctly label (poorly reconstructed) the Superhard materials as anomalies. In our case we want the features to be poorly reconstructed for auxetic materials. Therefore in an unsupervised model, the only parameters that can be tuned are the features and the network architecture, since there is no target quantity to fit, unlike supervised learning. Therefore finding the optimal set of features and network architecture that will best represent the target quantity can be a daunting task. To ease this we employ a semi-supervised learning approach in the sense that during the training of the Autoencoder, it is only fed with normal class(non-auxetic) samples in the hope that it learns the patterns related to the normal class in a better way. This would result in the auxetic materials being reconstructed poorly and thereby distinguishing them will be easier 31 .