Developing a molecular-level understanding
of the properties
of
water is central to numerous scientific and technological applications.
However, accurately modeling water through computer simulations has
been a significant challenge due to the complex nature of the hydrogen-bonding
network that water molecules form under different thermodynamic conditions.
This complexity has led to over five decades of research and many
modeling attempts. The introduction of the MB-pol data-driven many-body
potential energy function marked a significant advancement toward
a universal molecular model capable of predicting the structural,
thermodynamic, dynamical, and spectroscopic properties of water across
all phases. By integrating physics-based and data-driven (i.e., machine-learned)
components, which correctly capture the delicate balance among different
many-body interactions, MB-pol achieves chemical and spectroscopic
accuracy, enabling realistic molecular simulations of water, from
gas-phase clusters to liquid water and ice. In this review, we present
a comprehensive overview of the data-driven many-body formalism adopted
by MB-pol, highlight the main results and predictions made from computer
simulations with MB-pol to date, and discuss the prospects for future
extensions to data-driven many-body potentials of generic and reactive
molecular systems.