Neural network (NN) based potential models offer an alternative that is flexible enough to describe accurately the size dependent structural and dynamical properties in these nano and sub-nanoscale metal clusters. More recently, NN based potential models are emerging as a popular alternative to the classical functional based models, as a result of the rapid developments happening in the computational resources in terms of better and faster hardware as well as the vast selections of electronic structure codes that facilitate us in the efficient generation of the structural training dataset. The primary goal of these training schemes is to train effective NN potentials to achieve the accuracy and precision of high-fidelity ab-initio methods at a fraction of their computational cost. An inherent limitation of these NN models is that they are interpolative. The traditional approach to resolve this problem has been a brute force one where the NN is trained against as large a training dataset as possible. Such large-scale generation of high-fidelity training data can become challenging depending on the level of the electronic structure calculations employed. To tackle the challenges faced in the generation of an optimised training dataset, there have been several recent efforts to develop active learning and reinforcement learning strategies that facilitate in the effective sampling and selection of the training structures for NN models. It is an effort to ensure that the trained NN describes the potential energy surface for the elements accurately with minimum training dataset. For example, an active learning (AL) strategy based on the Query by Committee (QBC) scheme 51 was developed by Smith et al. which uses the disagreement between an ensemble of ML potentials to probe the reliability of the model's prediction. This approach allows for automatic sampling of the regions of chemical space which was not being described accurately by the ML potential model. It was validated over a test dataset of a diverse set of organic molecules and their results showed that one requires only 10% to 25% of the data to accurately represent the chemical space of these molecules. In another approach an AL scheme (deep potential generator (DP-GEN)) that constructs ML models for simulating materials at the molecular scale was introduced by Zhang et al. Their procedure involve exploration, generation of accurate reference data, and training. They used Al and Al-Mg as two representative cases and showed that accurate ML models can be trained with minimum number of reference data. In a recent work, unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the Gaussian Approximation Potential (GAP) model, which used dataset generated from a "meltquench" ab initio molecular dynamics (AIMD) was demonstrated by Sivaraman et al. From the trajectories of both melt and quench cycles of the AIMD simulations, the underlying cluster structures were exploited by partitioning them to "N" uncorrelated clusters. This modified dataset was sequentially sampled to train the GAP model until the desired accuracy was achieved. In another approach Vandermause et al. uses structures sampled on-the-fly from AIMD and an adaptive Bayesian inference method to automate the training of low-dimensional multiple element interatomic force field models. Their AL framework uses inherent uncertainty of a Gaussian process regression model to decide acceptance of model prediction or the need to augment training data. In all of the above studies, the overarching aim in these studies is to minimize the ab-initio training data required to describe the potential energy surface.