These interactions are relatively weak compared to the energy variations in chemical bonds, and so the development of more reliable LJ FFs for adsorption in MOFs remains an open and important challenge. As with most areas of material science, ML has a possible role to play here. The interatomic potentials which describe short range interactions can be constructed using ML methods by approximating the high-dimensional potential energy surface (PES) using training data obtained from ab initio calculations. The general and flexible nature of neural network models makes them a viable ML architecture for this purpose. Indeed, the so-called high-dimensional neural network potential (HDNNP) was introduced as early as 2007 by Behler and Parrinello, and in principle considers the PES to be a sum of environment-dependent atomic energy contributions. HDNNPs can be computed many orders of magnitude faster than e.g., DFT calculations, they retain the accuracy of reference ab initio data, and can be scaled to large systems or time scales. These concepts are only beginning to emerge in the field of MOFs. The first application of HDNNPs for MOFs was introduced by Eckhoff and Behler, where they predicted the negative thermal expansion of MOF-5 as well as its phonon density of states using HDNNPs. Zheng et al. also recently developed a HDNNP to study to the chemisorption and diffusion of CO2 in Mg-MOF-74. While promising, this approach comes with its own set of open challenges. These include, but are not limited to, the construction of more informative feature vectors which can capture both the local chemical environments and MOF pore attributes, in developing an approach which can model material classes with a large number of chemical elements (the feature vector often scales with the number of chemical elements ), and in generating the reference training data for MOFs with a large number of atoms in their unit cells. Furthermore, extending this approach to predict adsorption in chemically diverse material databases would require transferable models: the training set must represent the problem at hand. A good example is the general-purpose ANI-2 potential, 100 which was trained on reference data for a large number of organic molecules and is thus capable of simulating systems containing (H, C, N, O, F, Cl, and S).