The Interatomic Potential (IP) was trained with the use of Neural Equivariant Interatomic Potentials (NequIP), an equivariant graph neural network approach for learning interatomic potentials from ab initio calculations for molecular dynamics simulations. NequIP represents a significant advance over prior non-equivariant models, by utilizing higher-order tensors instead of relying solely on scalar features. This allows NequIP to capture the complexities of molecular systems more effectively, encoding bond angles, dihedral angles, and other angularly dependent features in a more flexible manner -something of high interest in this work, given the strongly directional nature of the interactions. Compared to other deep neural network models, this approach is more efficient, requiring a smaller training set to develop accurate Machine-Learned Interatomic Potentials (MLIPs).