  Compositionally complex alloys or concentrated solid solutions are the latest
frontier in catalyst design, but mixing different elements in one catalyst may
result in surface segregation. Atomistic simulations can predict segregation
patterns, but standard approaches based on mean-field models, cluster
expansion, or classical interatomic potentials are often limited for the
description of multicomponent alloys. We present machine learning potentials
that can describe surface segregation with near DFT accuracy. The method is
used to study a complex Co-Cu-Fe-Mo-Ni quinary alloy. For this alloy, an
unexpected segregation of Co, which has a relatively high surface energy, is
observed. We rationalize this surprising mechanism in terms of simple
transition-metal chemistry.
