  Metallic alloys often form phases - known as solid solutions - in which
chemical elements are spread out on the same crystal lattice in an almost
random manner. The tendency of certain chemical motifs to be more common than
others is known as chemical short-range order (SRO) and it has received
substantial consideration in alloys with multiple chemical elements present in
large concentrations due to their extreme configurational complexity (e.g.,
high-entropy alloys). Short-range order renders solid solutions "slightly less
random than completely random", which is a physically intuitive picture, but
not easily quantifiable due to the sheer number of possible chemical motifs and
their subtle spatial distribution on the lattice. Here we present a multiscale
method to predict and quantify the SRO state of an alloy with atomic
resolution, incorporating machine learning techniques to bridge the gap between
electronic-structure calculations and the characteristic length scale of SRO.
The result is an approach capable of predicting SRO length scale in agreement
with experimental measurements while comprehensively correlating SRO with
fundamental quantities such as local lattice distortions. This work advances
the quantitative understanding of solid-solution phases, paving the way for SRO
rigorous incorporation into predictive mechanical and thermodynamic models.
