Traditional binary classification problems are typically characterised as supervised learning tasks focusing on distinguishing between two well-represented classes. Nevertheless, building traditional classifiers becomes challenging when one of the classes is significantly underrepresented. One-class classification (OCC) is applicable in such cases. It aims to build a model of a single normal class, and to identify instances that do not conform to this normal class as anomalies based exclusively on the information from this normal category. OCC plays a critical role in various practical applications, including domains such as cybersecurity, quality control, and fault detection. However, its utilisation in material discovery and research has been relatively limited. This limitation can be attributed to several factors. Firstly, materials science problems often involve multi-class problems, particularly in the field of property prediction. Secondly, the intricate nature and characteristics of materials add an extra layer of complexity, rendering the application of classification models even more challenging. Most importantly, while OCC models require only one class of data for training (e.g. the composition of materials which can be synthesised), it is still essential to include samples which lie outside of this class for validation (e.g. compositions where materials cannot be synthesised). In materials research, obtaining negative samples are particularly difficult due to the intricacies of the experimental and testing process, and the tendency not to report "failed" synthetic experiments in literature. Therefore, an inherent challenge posed by such OCC problems characterised by the paucity of negative samples for both training and validation lies in evaluating the performance of machine learning (ML) models, as conventional evaluation metrics cannot be applied.