While there exist a wide variety of regression models and decision tree-based searches, Graph Neural Networks (GNNs) have shown superior performance in predicting material properties with a large amount of training data required for learning flexible structural representations. GNNs, however, lack interpretability and do not work well with smaller datasets, which may limit their practical applicability . The unified task-specific ranking of features can also be utilized to enhance the performance of GNNs on smaller datasets. One way to enhance the accuracy of these models when dealing with limited data is by training them on features that are weighted based on their significance in contributing to the task. This approach can be particularly beneficial for practical applications where gathering data is costly.