  The reactivity of silicates in an aqueous solution is relevant to various
chemistries ranging from silicate minerals in geology, to the C-S-H phase in
cement, nanoporous zeolite catalysts, or highly porous precipitated silica.
While simulations of chemical reactions can provide insight at the molecular
level, balancing accuracy and scale in reactive simulations in the condensed
phase is a challenge. Here, we demonstrate how a machine-learning reactive
interatomic potential can accurately capture silicate-water reactivity. The
model was trained on a new dataset comprising 400,000 energies and forces of
molecular clusters at the $\omega$-B97XD def2-TVZP level. To ensure the
robustness of the model, we introduce a new and general active learning
strategy based on the attribution of the model uncertainty, that automatically
isolates uncertain regions of bulk simulations to be calculated as small-sized
clusters. Our trained potential is found to reproduce static and dynamic
properties of liquid water and solid crystalline silicates, despite having been
trained exclusively on cluster data. Furthermore, we utilize enhanced sampling
simulations to recover the self-ionization reactivity of water accurately, and
the acidity of silicate oligomers, and lastly study the silicate dimerization
reaction in a water solution at neutral conditions and find that the reaction
occurs through a flanking mechanism.
