  There is intense interest in uncovering design rules that govern the
formation of various structural phases as a function of chemical composition in
multi-principal element alloys (MPEAs). In this paper, we develop a machine
learning (ML) approach built on the foundations of ensemble learning, post hoc
model interpretability of black-box models and clustering analysis to establish
a quantitative relationship between the chemical composition and experimentally
observed phases of MPEAs. The novelty of our work stems from performing
instance-level (or local) variable attribution analysis of ML predictions based
on the breakdown method, and then identifying similar instances based on
k-means clustering analysis of the breakdown results. We also complement the
breakdown analysis with Ceteris Paribus profiles that showcase how the model
response changes as a function of a single variable, when the values of all
other variables are fixed. Results from local model interpretability analysis
uncover key insights into variables that govern the formation of each phase.
Our developed approach is generic, model-agnostic, and valuable to explain the
insights learned by the black-box models. An interactive web application is
developed to facilitate model sharing and accelerate the design of novel MPEAs
with targeted properties
