Despite the dramatic increase in the use of MLPs for both molecules and materials, only seven articles proposed MLPs for MOFs at the time of writing, all of them consisting of neural network potentials (NNP). MOFs are particularly challenging for multiple reasons, in particular because of the different interactions that need to be correctly modeled -from strong covalent bonds and ionic interactions to weak van der Waals interactions -, along with their chemically complex composition. In addition, the (often) large unit cell size impedes ab initio simulations from generating training data. In 2019, Eckhoff and Behler proposed the first MLP for a MOF system (MOF-5) and circumvented the need for large ab initio calculations by using DFT calculations of small molecular fragments to train their MLP for the periodic bulk framework. This method was applied on different systems in three later works in different groups. However, the molecular fragments approach suffers from a lack of universal method to choose the fragments working on all MOFs, and from the sensitivity of its predicted energies to the choice of fragments. To overcome its shortcomings, four other works relied on periodic DFT calculations on the primitive cell of the framework to train their NNP. An option proposed by Vandenhaute et al. to solve the high computational cost of running ab initio simulations with large unit cells, was to use a dataefficient NNP (NequIP 32 ) to reduce the number of ab initio calculations. Interestingly, this work also demonstrated that it was possible to simulate a phase transition for MOFs with MLPs, although the transition was non-displacive and did not involve any bond breaking.