Machine-learning-driven molecular dynamics, powered by machine learning potentials (MLPs), is transforming the simulation of aqueous systems. By overcoming the long-standing trade-off between accuracy and efficiency inherent to traditional approaches, MLPs enable molecular simulations that achieve near-ab initio accuracy while reaching spatial and temporal scales relevant to complex water phenomena, such as hydrogen-bond dynamics, ion solvation, and chemical reactivity. This review synthesizes recent methodological and conceptual advances in MLP-based simulations of bulk water, aqueous solutions, and confined or interfacial environments, as well as reactive processes such as proton transfer and acid-base chemistry. Together, these developments establish MLPs as a unifying framework for elucidating the physics and chemistry of aqueous systems.
Li et al. (Thu,) studied this question.