  Development of process-structure-property relationships in materials science
is an important and challenging frontier which promises improved materials and
reduced time and cost in production. Refractory high entropy alloys (RHEAs) are
a class of materials that are capable of excellent hightemperature properties.
However, due to their multi-component nature, RHEAs have a vast composition
space which presents challenges for traditional experimental exploration. Here,
quantitative models of compressive yield strength and room-temperature
plasticity are developed through a deep learning approach. Uncertainty
quantification is performed through a variety of statistical validation
techniques. Model predictions are experimentally validated through collection
of recent literature and the synthesis and experimental characterization of two
new, unreported RHEAs: AlMoTaTiZr and Al0.239Mo0.123Ta0.095Ti0.342Zr0.201.
Finally, through the application of model interpretability, features having the
greatest impact on both the mechanical property and uncertainty of the deep
learning models are revealed, and shown to agree well with current physics and
materials science theory.
