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Figure 7. Characterization of heterogeneous deformation induced strengthening through various experimental techniques. (A) Phase
map and kernel average misorientation map of electron back scattered diffraction (EBSD) results in the Al Co Fe Ni EHEA.
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Reproduced from Ref. . CC BY 4.0; (B) bright-field images of transmission electron microscopy (TEM) in the AlCoCrFeNi EHEA.
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Reproduced with permission from Huang et al. . Copyright 2021, Elsevier; (C) SEM image and (D) strain map of digital image
correlation (DIC). A: deformed region; B: large grain; C: small grain; (E and F) loading-unloading-reloading (LUR) curves in the
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Al CoCrFeNi Ti HEA. Reproduced with permission from He et al. . Copyright 2021, Elsevier.
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navigating the multi-dimensional compositional space. Therefore, people turned to the data-driven
approach, such as ML modeling, which is supposed to be more effective in locating the eutectic
compositions in the complex compositional space. However, the lack of sufficient high-fidelity EHEA data,
the imbalanced database, and the poor design of data descriptors can compromise the performance of the
ML models, which warrants further research efforts in this field. Finally, we also discuss the various
strengthening mechanisms derived from the eutectic microstructure and compositional complexity in
EHEAs (i.e., low stacking fault energy). These prior works indicate that the data-based design of EHEAs is
promising but still at its infant stage.