On the other hand, applications of anomaly detection algorithms have increased rapidly to address real-world issues like fraud detection in banks , cyber threats 23 , and disease variant detection . Historically, many of these algorithms were designed to find outliers to be removed. In a recent report, Ziyan et al. introduced an anomaly detection model to the material community for the first time. The fact that the radial distribution function is poorly reconstructed for superhard materials is the basis for their unsupervised anomaly detection model. In a recent article, Joshua et al . proposed how ML can be utilized to get the exceptions and anomalies that exist in chemical and material science. In this study, we aim to build a general model using supervised machine learning algorithms to predict Poisson's ratio. We observe that the accuracy varies drastically based on whether the auxetics materials are used in the dataset or not. Moreover, the presence of homogenous auxetic material in nature is pretty low. So we treat the NPR materials as anomalies. Therefore we build an Auto-encoder Neural Network (ANN) based semi-supervised anomaly detection model where labeled data is used to train the Autoencoder. Materials with only a positive Poisson's ratio are used for training so that the auto-encoder fails to reconstruct the auxetic materials. Based on the reconstruction loss, we can identify our anomalies, the NPR materials. We show that the precision of this anomaly detection model is 0.64 with a F1-score of 0.56. We also build a supervised regression model to predict Poisson's ratio with an R 2 0.82 applicable to general non-auxetic materials. The supervised model is used to determine the optimal descriptors for learning the pattern of Poisson's ratio. This in turn helps in optimizing both the supervised and the anomaly detection model.