  Multi-principal element alloys open large composition spaces for alloy
development. The large compositional space necessitates rapid synthesis and
characterization to identify promising materials, as well as predictive
strategies for alloy design. Additive manufacturing via directed energy
deposition is demonstrated as a high-throughput technique for synthesizing
alloys in the Cr-Fe-Mn-Ni quaternary system. More than 100 compositions are
synthesized in a week, exploring a broad range of compositional space. Uniform
compositional control to within +/-5 at% is achievable. The rapid synthesis is
combined with conjoint sample heat treatment (25 samples vs 1 sample), and
automated characterization including X-ray diffraction, energy-dispersive X-ray
spectroscopy, and nano-hardness measurements. The datasets of measured
properties are then used for a predictive strengthening model using an active
machine learning algorithm that balances exploitation and exploration. A
learned parameter that represents lattice distortion is trained using the alloy
compositions. This combination of rapid synthesis, characterization, and active
learning model results in new alloys that are significantly stronger than
previous investigated alloys.
