  Simulation techniques based on accurate and efficient representations of
potential energy surfaces are urgently needed for the understanding of complex
aqueous systems such as solid-liquid interfaces. Here, we present a machine
learning framework that enables the efficient development and validation of
models for complex aqueous systems. Instead of trying to deliver a
globally-optimal machine learning potential, we propose to develop models
applicable to specific thermodynamic state points in a simple and user-friendly
process. After an initial ab initio simulation, a machine learning potential is
constructed with minimum human effort through a data-driven active learning
protocol. Such models can afterwards be applied in exhaustive simulations to
provide reliable answers for the scientific question at hand. We showcase this
methodology on a diverse set of aqueous systems with increasing degrees of
complexity. The systems chosen here comprise bulk water with different ions in
solution, water on a titanium dioxide surface, as well as water confined in
nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of
our approach with respect to the underlying ab initio reference, the resulting
models are evaluated in detail with an automated validation protocol that
includes structural and dynamical properties and the precision of the force
prediction of the models. Finally, we demonstrate the capabilities of our
approach for the description of water on the rutile titanium dioxide (110)
surface to analyze the structure and mobility of water on this surface. Such
machine learning models provide a straightforward and uncomplicated but
accurate extension of simulation time and length scales for complex systems.
