  We present a highly accurate and transferable parameterization of water using
the atomic cluster expansion (ACE). To efficiently sample liquid water, we
propose a novel approach that involves sampling static calculations of various
ice phases and utilizing the active learning (AL) feature of ACE-based
D-optimality algorithm to select relevant liquid water configurations,
bypassing computationally intensive ab-initio molecular dynamics (AIMD)
simulations. Our results demonstrate that the ACE descriptors enable a
potential initially-fitted solely on ice structures which is later upfitted
with few configurations of liquid, identified with active learning to provide
an excellent description of liquid water. The developed potential exhibits
remarkable agreement with first-principles reference, accurately capturing
various properties of liquid water, including structural characteristics such
as pair correlation functions, covalent bonding profiles, and hydrogen bonding
profiles, as well as dynamic properties like the vibrational density of states,
diffusion coefficient and thermodynamic properties such as the melting point of
the ice Ih. Our research introduces a new and efficient sampling technique for
machine learning potentials in water simulations, while also presenting a
transferable interatomic potential for water that reveals the accuracy of first
principles reference. This advancement not only enhances our understanding the
relationship between ice and liquid water at the atomic level, but also opens
up new avenues for studying complex aqueous systems.
