Deep Potential for Interaction between Hydrated Cs+ and Graphene

The influence of hydrated cation–π interaction forces on the adsorption and filtration capabilities of graphene-based membrane materials is significant. However, the lack of interaction potential between hydrated Cs⁺ and graphene limits the scope of adsorption studies. Here, it is developed that a deep neural network potential function model to predict the interaction force between hydrated Cs⁺ and graphene. The deep potential has DFT-level accuracy, enabling accurate property prediction. This deep potential is employed to investigate the properties of the graphene surface solution, including the vibrational power spectrum of water density distribution, radial distribution function, and mean square displacement. Furthermore, the adsorption energy and charge between hydrated Cs⁺ and graphene were calculated for varying amounts of bound water, indicating that the presence of water molecules weakens the interaction between the ions and graphene. The method provides a powerful tool to study the adsorption behavior of hydrated cations on graphene surfaces and offers a new solution for handling radionuclides.