  A breakthrough in alloy design often requires comprehensive understanding in
complex multi-component/multi-phase systems to generate novel material
hypotheses. We introduce a modern data analytics workflow that leverages
high-quality experimental data augmented with advanced features obtained from
high-fidelity models. Herein, we use an example of a consistently-measured
creep dataset of developmental high-temperature alloy combined with scientific
alloy features populated from a high-throughput computational thermodynamic
approach. Extensive correlation analyses provide ranking insights for most
impactful alloy features for creep resistance, evaluated from a large set of
candidate features suggested by domain experts. We also show that we can
accurately train machine learning models by integrating high-ranking features
obtained from correlation analyses. The demonstrated approach can be extended
beyond incorporating thermodynamic features, with input from domain experts
used to compile lists of features from other alloy physics, such as diffusion
kinetics and microstructure evolution.
