The theory of quantum mechanics has been well-developed over the last hundred years. However, its application is limited by the computational power of modern computers. With the rise of Big Data and Artificial Intelligence, a new door is opening to us to untangle the fascinating world of quantum mechanics. In our lab, we use Diffusion Monte Carlo (DMC), a statistical simulation to solve molecular vibration and rotation problems. It is remarkably accurate and versatile, making it suited for notoriously difficult systems, like protonated methane (CH5+). Yet, it requires millions of potential energy evaluations before quality results can be acquired, which often takes unrealistic amounts of time. In this work, we use TensorFlow, a neural network training framework developed by Google, with full Graphics processing unit (GPU)-acceleration support, to considerably speed up the evaluation of the potential energies needed for the DMC calculations. We started by running a small-scale conventional DMC simulation to collect a set of molecular configurations and their corresponding potential energies, which are then fed into a 3-layer deep neural network on Tensorflow with carefully-selected parameters. Once finished training, the neural network can replace the conventional potential energy evaluation method used in DMC to greatly expedite the process. We tested this model on water(H2O), protonated methane(CH5+) and water dimer((H2O)2), and was able to achieve a 15-fold acceleration, with less than 0.01% error compared to conventional methods. Our future goal is to further optimize the neural network to make it even faster and more accurate, then apply it to larger systems which were unsolvable before due to their computationally intractable time.