  While first-principles methods have been successfully applied to characterize
individual properties of multi-principal element alloys (MPEA), their use to
search for optimal trade-offs between competing properties is hampered by high
computational demands. In this work, we present a novel framework to explore
Pareto-optimal compositions by integrating advanced ab initio-based techniques
into a Bayesian multi-objective optimization workflow complemented with a
simple analytical model providing straightforward analysis of trends. We
benchmark the framework by applying it to solid solution strengthening and
ductility of refractory MPEAs, with the parameters of the strengthening and
ductility models being efficiently computed using a combination of the
coherent-potential approximation method, accounting for finite-temperature
effects, and actively-learned moment-tensor potentials parameterized with ab
initio data. Properties obtained from ab initio calculations are subsequently
used to extend predictions of all relevant material properties to a large class
of refractory alloys with the help of the analytical model validated by the
data and relying on a few element-specific parameters and universal functions
that describe bonding between elements. Our findings offer new crucial insights
into the traditional strength-vs-ductility dilemma of refractory MPEAs. The
proposed framework is versatile and can be extended to other materials and
properties of interest, enabling a predictive and tractable high-throughput
screening of Pareto-optimal MPEAs over the entire composition space.
