Machine learning potential has drawn more and more attention in the recent years due to its ability in enabling automatic construction of high-fidelity force field potentials. They allow the successful application of reliable high throughput atomistic simulations in accelerating material discovery and design with novel functions and properties. Those methods circumvent the parameterization problem as faced by most empirical potentials and decrease model bias by using machine learning techniques to interpolate the highly nonlinear potential energy surface based on first principle calculations. Despite the advantages such as a well automated process and high flexibility of model functions, systematic uncertainty quantifications are still missing to develop uncertainty management techniques for the quantification and reduction of uncertainty involved in the developed machine learning interpolated atomic potential energy surface. In this paper, the uncertainty resources associated with the development of machine learning potential will be identified. Corresponding uncertainty quantification will be conducted through a systematic sensitivity analysis to investigate the reliability of the developed machine learning potential for graphene, an emergent promising 2D material.