  In this paper, a synergistic computational/experimental approach is presented
for the rapid discovery and characterization of novel alloys within the
compositionally complex (i.e., "medium/high entropy") refractory alloy space of
Ti-V-Nb-Mo-Hf-Ta-W. This was demonstrated via a material design cycle aimed at
simultaneously maximizing the objective properties of high specific hardness
(hardness normalized by density) and high specific elastic modulus (elastic
modulus normalized by density). This framework utilizes high-throughput
computational thermodynamics and intelligent filtering to first reduce the
untenably large alloy space to a feasible size, followed by an iterative design
cycle comprised of high-throughput synthesis, processing, and characterization
in batch sizes of 24 alloys. After the first iteration, Bayesian optimization
was utilized to inform selection of the next batch of 24 alloys. This paper
demonstrates the benefit of using batch Bayesian optimization (BBO) in material
design, as significant gains in the objective properties were observed after
only two iterations or "shots" of the design cycle without using any prior
knowledge or physical models of how the objective properties relate to the
design inputs (i.e., composition). Specifically, the hypervolume of the Pareto
front increased by 54% between the first and second iterations. Furthermore, 10
of the 24 alloys in the second iteration dominated all alloys from the first
iteration.
