The neural network potential (NNP) developed here describes aqueous NaCl inside graphene slit pores across a range of slit heights and ion concentrations. Our approach for generating the model is described in the SI, along with extensive validation studies demonstrating that the model satisfactorily reproduces the potential energy surface of the underlying DFT at the revPBE-D3 level of theory. We used the model to perform largescale neural network potential-based molecular dynamics (NNP-MD) simulations of 1M electrolytes confined to graphene slit pores of four heights: H = 6.70, 10.05, 13.40, and 16.75 Å. These heights correspond to removing one to four carbon layers from graphite and are thus commensurate with the types of pores which can be realized experimentally using van der Waals assembly, where atomically flat sheets are separated by spacers of one or more graphene layers. 46 The slit heights studied here range from the extremely confined limit of a monolayer of solution to a system with approximately four layers of water, as shown by the density profiles and snapshots given in Fig. . The ions in these electrolytes, particularly chloride, interact 5 strongly with the graphene surfaces, with a significant fraction of ions directly adsorbed.