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Page 6 of 45 Mooraj et al. J Mater Inf 2023;3:4 https://dx.doi.org/10.20517/jmi.2022.41

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Figure 3. Schematic illustration of properties of HEAs. This figure is quoted with permission from Li et al. , copyright 2021, John Wiley
and Sons. HEA: High-entropy alloy.

Sluggish diffusion
Diffusion through HEAs can be much slower than diffusion in conventional alloys. Many researchers have
investigated the elemental diffusion in HEA systems and have found that the diffusivities are often much
lower than those in binary or dilute alloy systems [70-72] . This sluggish diffusion can improve the stability of
solid solution phases as harmful intermetallic phases can be largely suppressed. Intermetallic phases can
only form under non-polymorphic solidification conditions, which require long-range diffusion.
Additionally, metastable solid solutions form under polymorphic crystallization conditions, which only
[23]
require topological atomic rearrangements on the atomic length scale . Thus, the sluggish diffusion in
HEAs suppresses the long-range diffusion that would lead to the formation of brittle intermetallic phases
and instead promotes polymorphic crystallization to form solid solutions. Additionally, the coarsening of
grains can be inhibited due to sluggish diffusion, leading to improved thermal stability and
thermomechanical performance at elevated temperatures [73-75] .

Cocktail effect
Dr. Ranganathan first proposed the cocktail effect to describe the synergistic nature of compositionally
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complex alloys . This effect describes the unexpected properties observed in HEAs, bulk metallic glasses,
[48]
and super-elastic and super-plastic metals (also called “gum” metals) . Unlike the other effects described
earlier, the cocktail effect does not predict the expected properties of HEAs. Still, it serves as a reminder that
certain elemental combinations can achieve synergistic effects that are not predicted from the base
constituent elements.

HIGH-THROUGHPUT COMPUTATIONAL METHODS TO DESIGN HEAS
As previously mentioned, the compositional space opened by the concept of HEAs is vast. This design space
is too large to explore through traditional trial-and-error means. Thus, it is of significant interest to identify
[77]
promising compositions and phases via high-throughput computational methods . These computational
methods include machine learning, first-principles calculations, molecular dynamics, and CALPHAD. The