  Materials composed of elements from the third and fifth columns of the
periodic table display a very rich behavior, with the phase diagram usually
containing a metallic liquid phase and a polar semiconducting solid. As a
consequence, it is very hard to achieve transferable empirical models of
interactions between the atoms that can reliably predict their behavior across
the temperature and composition range that is relevant to the study of the
synthesis and properties of III/V nanostructures and devices. We present a
machine-learning potential trained on density functional theory reference data
that provides a general-purpose model for the Ga$_x$As$_{1-x}$ system. We
provide a series of stringent tests that showcase the accuracy of the
potential, and its applicability across the whole binary phase space, computing
with ab initio accuracy a large number of finite-temperature properties as well
as the location of phase boundaries. We also show how a committe model can be
used to reliably determine the uncertainty induced by the limitations of the ML
model on its predictions, to identify regions of phase space that are predicted
with insufficient accuracy, and to iteratively refine the training set to
achieve consistent, reliable modeling.
