For the prediction of uncertainty associated with an ML potential, we have used deep ensembles of ANI models. We tuned three key hyperparameters to optimize the performance of the ANI models within the ensemble. This tuning process allowed us to construct a deep ensemble of eight neural networks, each trained with hyperparameter variations to capture a range of predictive uncertainties in the ML potential. The model provides a mapping from sets of atomic species and positions {(𝑍 𝑖 , 𝑟 ⃗ 𝑖 )} to potential energy E and interatomic forces {(𝐹 ⃗ 𝑖 )}. The potential energy is modeled as a sum over the atomic contributions E i :