  Additively manufactured (AM) structural components with complex geometries
and tailored properties at voxel-size resolution will lead to significant leap
in performance in various critical engineering applications. However, at each
voxel, we first need to be able to design the alloy efficiently and reliably.
We demonstrate a hybrid approach combining calculation of phase diagram
(CALPHAD)-based integrated computational materials engineering (ICME) with
machine learning and inverse design techniques and performed a full alloy
design cycle of a novel Al alloy (Al-Er-Zr-Y-Yb-Ni) for AM from virtual
predictions to experimental validation. We designed this alloy to exhibit high
tensile strength at room temperature through nanoscale L1$_2$-phase
precipitation which stabilizes the microstructure to maintain strength after
high-temperature aging. We initially exploit a fine distribution of metastable
eutectic ternary phases through rapid solidification, which serve as the source
for the reactive elements enabling nanoscale precipitation of a high phase
fraction of the thermally stable L1$_2$ strengthening phases. The strength of
the 3D-printed samples manufactured via laser powder bed fusion (LPBF) from the
designed composition is comparable to that of wrought Al 7075, and after
high-temperature (400$^\circ$C) aging is 50% stronger than the best benchmark
printable Al alloy1. The stable strengthening strategy is applicable to a wide
range of alloys and rapid solidification processes, and our hybrid ML/CALPHAD
numerical framework can be used for the efficient and robust design of alloy
microstructures and properties, expanding the capabilities of additive as well
as traditional manufacturing.
