  High-entropy alloys (HEAs), containing several metallic elements in
near-equimolar proportions, have long been of interest for their unique
mechanical properties. More recently, they have emerged as a promising platform
for the development of novel heterogeneous catalysts, because of the large
design space, and the synergistic effects between their components. In this
work we use a machine-learning potential that can model simultaneously up to 25
transition metals to study the tendency of different elements to segregate at
the surface of a HEA. We use as a starting point a potential that was
previously developed using exclusively crystalline bulk phases, and show that,
thanks to the physically-inspired functional form of the model, adding a much
smaller number of defective configurations makes it capable of describing
surface phenomena. We then present several computational studies of surface
segregation, including both a simulation of a 25-element alloy, that provides a
rough estimate of the relative surface propensity of the various elements, and
targeted studies of CoCrFeMnNi and IrFeCoNiCu, which provide further validation
of the model, and insights to guide the modeling and design of alloys for
heterogeneous catalysis.
