  The allotropes of boron continue to challenge structural elucidation and
solid-state theory. Here we use machine learning combined with random structure
searching (RSS) algorithms to systematically construct an interatomic potential
for boron. Starting from ensembles of randomized atomic configurations, we use
alternating single-point quantum-mechanical energy and force computations,
Gaussian approximation potential (GAP) fitting, and GAP-driven RSS to
iteratively generate a representation of the element's potential-energy
surface. Beyond the total energies of the very different boron allotropes, our
model readily provides atom-resolved, local energies and thus deepened insight
into the frustrated $\beta$-rhombohedral boron structure. Our results open the
door for the efficient and automated generation of GAPs and other
machine-learning-based interatomic potentials, and suggest their usefulness as
a tool for materials discovery.
