Neural network potentials (NNPs) have recently become a vital component of the computational materials toolkit . They are used to parameterize the potential energy surface (PES) of an atomic system by leveraging machine learning techniques to model it as a function of local environment descriptors. NNPs generally achieve superior accuracy in computed energies and forces compared to traditional inter-atomic potentials, while also facilitating precise and efficient simulations over length and timescales that exceed the capabilities of ab initio methods. Graph neural networks (GNNs) enable a natural representation of molecules and crystals, wherein nodes and edges represent atoms and the bonds between them. We use a GNN trained by the Materials Project , the Materials 3-body Graph Network (M3GNet) architecture . It is trained on more than 187,000 energies, 16,000,000 forces and 1,600,000 stresses from DFT structural relaxations using PBE or the GGA+U method and is applicable for 89 elements of the periodic table with low energy, force and stress errors. It can be used for structural relaxations, molecular dynamics simulations, etc. M3GNet's training set does not include van der Waals effects, and so the computed binding energy deltas we consider will likely be weaker (less negative). In our work, a slight improvement is made to the M3GNet source code to enable structural relaxation of crystal structures with fixed cell symmetry while both the cell volume and atomic positions of atoms are allowed to move.