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Page 36 of 45 Mooraj et al. J Mater Inf 2023;3:4 https://dx.doi.org/10.20517/jmi.2022.41

Figure 18. (A) Weight gain of Mo VNbTiCr after corrosion test in superheated steam at 400 °C at 10.3 MPa pressure for 70 days,
x
0.5
Zr-4 alloy is provided for comparison. This figure is quoted with permission from Xiang et al. [210] , copyright 2020, Elsevier;
(B) potentiodynamic polarization curves of Al CrFeCo CuNiTi HEA compared to Q235 steel. This figure is quoted with permission from
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x
Qiu et al. [212] , copyright 2019, Elsevier; (C) potentiodynamic polarization curves of Al CrFeCoCuTiNi HEAs and Q235 steel substrate.
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2
This figure is quoted with permission from Qiu et al. [213] , copyright 2013, Elsevier; (D) potentiodynamic polarization curves of
Ti ZrNbTaMo HEAs and Ti6Al4V. This figure is quoted with permission from Hua et al. [214] , copyright 2021, Elsevier.
x
surface analysis via XPS showed that the surface film of the HEAs is mainly composed of the Ti , Zr , Nb ,
4+
4+
5+
Ta , Mo , and Mo oxides, which indicates the formation of a passivation layer that protected the alloys
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4+
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from severe corrosion.
CONCLUSIONS AND FUTURE OUTLOOK
HEAs present abundant opportunities to search for new materials with properties and performance that can
exceed traditional dilute alloys. While the potential for this new class of materials is promising, the vast
composition and microstructure space is too large to explore efficiently via traditional metallurgical
techniques based on trial-and-error approaches. This review article highlights important advances in
combinatorial studies that either present high-throughput methods to rapidly filter out undesirable
materials or provide insights into general rules of thumb to allow researchers to design high-performance
materials more efficiently.

The ultimate goal is to ensure that researchers spend more time understanding how to design and
manufacture high-performance HEAs for industrial applications and less time on repetitive sample
preparation and characterization methods. Implementing efficient high-throughput methods can minimize
the time spent studying sub-optimal alloy compositions, which maximizes the resources spent on
improving the most promising alloys. First, this review explores the high-throughput computational
techniques that can down-select the design space before experimental characterization is even attempted.
Then, it presents works that use additive manufacturing as a solution to produce large combinatorial
libraries of bulk sample materials at length scales comparable to those expected during service and