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high entropy materials with unusual structural and functional properties. Aside
from alloys, this “high entropy” notion was recently extended to intermetallics
and ceramics. Nevertheless, as the number of principal elements increases, the
total number of possible compositions can quickly rise to an astronomical value,

which defies the traditional “trial-and-error” approach that fixates one composition
at a time. Therefore, it is already a consensus in this field that machine learning
approaches become crucial to the research of high entropy materials, which can
greatly accelerate compositional screening, alloy design and development, and even

applications by learning from the big data accumulated in the literature over the past
decades.

In this special issue, we will emphasize the use of machine learning approaches in

tackling the challenging issues in the field of high entropy materials. We welcome
original contributions as well as topical reviews. The topics that we are going to
cover include, but are not limited to, the following:

● Development of high throughput experimental/computational methods for the
establishment of high-fidelity databases

● Machine learning-enabled structural characterizations for high entropy materials

● Machine learning-enabled understanding of thermodynamics and kinetics in high
entropy materials

● Machine learning guided the design and development of high entropy materials (i.e.,
compositional design, processing design, microstructural characterization, etc.)

● Machine learning guided applications of high entropy materials (i.e., structural

versus functional applications)