  We present a general-purpose machine learning Gaussian approximation
potential (GAP) for iron that is applicable to all bulk crystal structures
found experimentally under diverse thermodynamic conditions, as well as
surfaces and nanoparticles (NPs). By studying its phase diagram, we show that
our GAP remains stable at extreme conditions, including those found in the
Earth's core. The new GAP is particularly accurate for the description of NPs.
We use it to identify new low-energy NPs, whose stability is verified by
performing density functional theory calculations on the GAP structures. Many
of these NPs are lower in energy than those previously available in the
literature up to $N_\text{atoms}=100$. We further extend the convex hull of
available stable structures to $N_\text{atoms}=200$. For these NPs, we study
characteristic surface atomic motifs using data clustering and low-dimensional
embedding techniques. With a few exceptions, e.g., at magic numbers
$N_\text{atoms}=59$, $65$, $76$ and $78$, we find that iron tends to form
irregularly shaped NPs without a dominant surface character or characteristic
atomic motif, and no reminiscence of crystalline features. We hypothesize that
the observed disorder stems from an intricate balance and competition between
the stable bulk motif formation, with bcc structure, and the stable surface
motif formation, with fcc structure. We expect these results to improve our
understanding of the fundamental properties and structure of low-dimensional
forms of iron, and to facilitate future work in the field of iron-based
catalysis.

Materials Science