  The use of computer simulations to study the properties of aqueous systems
is, today more than ever, an active area of research. In this context, during
the last decade there has been a tremendous growth in the use of data-driven
approaches to develop more accurate potentials for water as well as to
characterize its complexity in chemical and biological contexts. We highlight
the progress, giving a historical context, on the path to the development of
many-body and reactive potentials to model aqueous chemistry, including the
role of machine learning strategies. We focus specifically on conceptual and
methodological challenges along the way in performing simulations that seek to
tackle problems in modeling the chemistry of aqueous solutions. In conclusion,
we summarize our perspectives on the use and integration of advanced
data-science techniques to provide chemical insights in physical chemistry and
how this will influence computer simulations of aqueous systems in the future.
