The need to accurately describe the potential energy surface of low dimensional systems including sub-nanometer clusters of transition metals with small number of atoms is critical given their exotic optical electronic magnetic as well as catalytic properties that are diverse and in many instances dependent on the cluster size. Their structural and dynamical properties are known to significantly different from their larger nanosized or bulk counterparts. This is attributed to the significantly higher surface to volume ratio, enhanced presence of dangling bonds, quantum confinement effect, as well as other electronic effects associated with the presence of d-orbitals which gives these clusters some unique chemistry . With greater advances in computational resources and advancements in ab initio calculations, high throughput computational approaches to materials design have been increasingly implemented in recent times. More recently, the advances in machine learning and data science have provided a new pathway to computationally explore the dynamical behavior as well as characterize the structure and energetics of these materials and accelerate the discovery of new materials for the various applications . A complete understanding of the energetic pathway, for example, would give us greater control over processes during an application and allow us to prevent undesirable side reactions .