AL is a widely used data selection technique that improves a model's performance by proactively selecting the high-error data points from a pool of unlabeled data . It aims to minimize exhaustive sampling by expanding the training set in areas where the ML model is most uncertain about its prediction. Among the various AL algorithms that have been developed to date, query-by-committee (QBC) uses disagreement between the models of the committee (ensemble) to identify regions where ML potentials fail to make a good prediction and then adds data to those sparsely explored regions in the chemical space. The regions with low ensemble uncertainty will typically arise when the current training data set included similar regions, such that every member of the ensemble is making an accurate inference.