for building E(3)-equivariant interatomic potentials, building on the e3nn library. It is a state-of-the-art message-passing neural network (MPNN) framework that has been shown to produce high accuracy potentials with good training data efficiency, due to its equivariance and because it directly operates on relative interatomic positions. It has demonstrated high accuracy across a wide variety of systems, including MOFs 29 and amorphous solids. As our goal is to produce large-scale models of amorphous systems, we also compared NequiP with Allegro, another approach of the same family and developed by the same group.