We chose MACE, an equivariant message-passing neural network framework (MPNN), which has been shown to perform similarly or better than eleven contemporary MLIPs on the small molecule rMD17 benchmark. Furthermore, MACE has demonstrated high accuracy and extrapolation capacity when compared to NequIP MPNN 58 on the 3BPA, and AcAc benchmarks. A subsequent study demonstrated MACE's excellent performance on a wider range of tasks: from improving on a global model's accuracy on the MD22 dataset of large molecules, to transferability to COMP6 benchmark by fitting to 10% of ANI-1x training set, to improving on the state of the art for many properties of the QM9 quantum chemistry benchmark, among others, as compared with several contemporary MLIPs. With respect to computational efficiency, a reasonably-sized MACE model has a latency of 24.3 ms per evaluation -around four times faster than other equivariant MPNN models. In MACE, atomic structures are treated as graphs, where nodes represent atoms in threedimensional space and any two nodes within a cutoff distance are connected. A number of message-passing iterations are performed to update the features associated with each node.