  The discovery of complex concentrated alloys has unveiled materials with
diverse atomic environments, prompting the exploration of solute segregation
beyond dilute alloys. Data-driven methods offer promising for modeling
segregation in such chemically complex environments, and are employed in this
study to understand segregation behavior of a refractory complex concentrated
alloy, NbMoTaW. A flexible methodology is developed that uses composable
computational modules, with different arrangements of these modules employed to
obtain site availabilities at absolute zero and the corresponding density of
states beyond the dilute limit, resulting in an extremely large dataset
containing 10 million data points. The artificial neural network developed here
can rely solely on descriptions of local atomic environments to predict
behavior at the dilute limit with very small errors, while the addition of
negative segregation instance classification allows any solute concentration
from zero up to the equiatomic concentration for ternary or quaternary alloys
to be modeled at room temperature. The machine learning model thus achieves a
significant speed advantage over traditional atomistic simulations, being four
orders of magnitude faster, while only experiencing a minimal reduction in
accuracy. This efficiency presents a powerful tool for rapid microstructural
and interfacial design in unseen domains. Scientifically, our approach reveals
a transition in the segregation behavior of Mo from unfavorable in simple
systems to favorable in complex environments. Additionally, increasing solute
concentration was observed to cause anti-segregation sites to begin to fill,
challenging conventional understanding and highlighting the complexity of
segregation dynamics in chemically complex environments.
