(364e) Specific Ion Effects in Aqueous Electrolyte Solutions from First Principles-Derived Machine-Learning Potentials

We investigate specific ion effects on the structure and dynamics of water in aqueous electrolyte solutions using atomistic machine-learning models constructed from the SCAN approximation of DFT predictions. Ion specificity of bulk electrolyte solutions following the Hofmeister series can induce wide-ranging effects on the dynamics of water - kosmotropic ions mitigate water mobility and chaotropic ions accelerate water molecules. Many existing studies of this phenomena apply conventional empirical models which are inherently limited in transferability and accuracy across a range of concentrations. In this work, in order to explore ion solvation characteristics with a first principles level of accuracy, we construct deep neural network models capable of learning highly complex and multi-dimensional interactions native to DFT representations. We find that these models overcome the limitations of conventional empirical models in representing water dynamics with concentration dependence. We then use the models to probe the underlying mechanisms of ion-induced water structure and mobility for a series of alkali halide ions (KCl, CsCl, NaBr, NaCl) in bulk solution.