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Chen et al. J Mater Inf 2022;2:19 https://dx.doi.org/10.20517/jmi.2022.23 Page 9 of 21

[69]
refractory HEAs . Li et al. rapidly fabricated continuously graded compositional libraries of Al CoCrFeNi
x
HEAs by a HT laser engineered net shaping process [70,71] . XRD, SEM, scanning transmission electron
microscopy (TEM) and nanoindentation were performed to analyze variations in the crystal structures,
microstructures and mechanical properties of these HEAs. Currently, some limitations in the HT additive
manufacturing method cannot be addressed. This methodology, however, has proven to be a high-efficiency
tool in HEA development, as well as further validating HT computational approaches.

To accelerate the exploration of HEAs with targeted properties or performance, Zhao et al. developed the
HT hot-isostatic-pressing-based micro-synthesis approach (HT-HIP-MSA), which can efficiently synthesize
and characterize 85 combinatorial alloys in a 13-principal element alloying space, as shown in
[72]
Figure 5F-H . Combined with theoretical computations, the HT-HIP-MSA can systematically and
economically investigate the composition-structure-property relationships of HEAs. Moreover, the in-situ
HT synthesis of FeCoNiCrCuAl was performed during TEM by Xu et al., where they recorded the dynamic
x [73]
melting process of FeCoNiCrCu with Al and examined the composition of FeCoNiCrCuAl by EDS . This
x
in-situ HT method provides a new strategy to produce HEA samples with high accuracy regarding
composition.

Conventional methods for the synthesis of HEAs, such as arc melting, laser cladding, thermal spray, spark
plasma sintering and ball milling, are too time-consuming for use in HT techniques for the accelerated
discovery of HEAs. To address this issue, our group reported a radio frequency inductively coupled plasma
[74]
(RF-ICP) method to synthesize HEAs in a rapid and HT fashion . The schematics of the experimental
setup and HEA synthesis process using the RF-ICP system are presented in Figure 6A and B. As shown in
Figure 6C and D, the time for HEA preparation was within 40 s and ~15 s were needed for cooling the
sample. It was found that the porosity of the Cu Ni binary alloy was significantly decreased by increasing
50
50
the healing time from 9 to 21 s. More importantly, high-purity FeCoNi-based alloys (Fe Co Ni ,
33 33 34
Fe Co Ni Cu , Fe Co Ni Cu Al and Fe Co Ni Cu Ti ) were also successfully synthesized with a low
25 25 25 25 20 20 20 20 20 20 20 20 20 20
level of defects. This methodology opens a new avenue to accelerate the compositional exploration of this
multidimensional alloy space. We also believe data scientists and metallurgists would be inspired to explore
new high-performance alloy systems with this methodology.

Huang et al. utilized eight or 28 sample holders of one electrode to simultaneously prepare eight or 28
[75]
different HEAs, respectively, in one batch of electrolysis under the same conditions . The prepared HEA
systems at different locations of the one cathode will not contaminate each other due to the insolubilization
of most transition metals and metal oxides in molten salts. Moreover, chemical solution deposition was
[76]
applied to prepare a library of [Ca (Nb Ta ) ] Bi O films with a total of 288 compositions . The surface
x 1-y y 1-x 1-z z δ
and cross-section microstructures of these designed systems were characterized using field-emission SEM.
A HT XRD system was applied to analyze the corresponding crystal structures using synchrotron radiation
with a wavelength of 0.8 Å along with a 2D detector (PILATUS) at the SPring-8 facility. This work indicates
that implementing HT conductivity measurements and HT XRD allowed us to increase the total
experimental throughput for exploring HEA materials.

The HT techniques of computation, synthesis, processing, characterization and data analysis to accelerate
the discovery of HEAs are well established. An integrated closed-loop process for HT HEA development,
however, has been demonstrated infrequently. Vecchio et al. developed a HT rapid experimental alloy
[77]
development (HT-READ) methodology, as shown in Figure 7 . CALPHAD and ML model-based
computational screening provided recommendations for composition selection and sample library design.
The designed samples were synthesized, processed, characterized, tested and analyzed in an automated HT