Aerospace & Defense Technology - October 2024 - 35

Tech Briefs
The goal is to use SRO as a " knob " to tailor material properties
by mixing chemical elements in high-entropy alloys in unique
ways. This approach has potential applications in industries
such as aerospace, biomedicine, and electronics, driving the
need to explore permutations and combinations of elements,
Cao says.
A Two-Pronged Machine Learning Solution
To study SRO using machine learning, it helps to picture the
crystal structure in high-entropy alloys as a connect-the-dots
game in a coloring book, Cao says.
" You need to know the rules for connecting the dots to see
the pattern. " And you need to capture the atomic interactions
with a simulation that is big enough to fit the entire pattern.
First, understanding the rules meant reproducing the chemical
bonds in high-entropy alloys. " There are small energy differences
in chemical patterns that lead to differences in shortrange
order, and we didn't have a good model to do that, "
Freitas says. The model the team developed is the first building
block in accurately quantifying SRO.
The second part of the challenge, ensuring that researchers
get the whole picture, was more complex. High-entropy
alloys can exhibit billions of chemical " motifs, " combinations
of arrangements of atoms. Identifying these motifs from simulation
data is difficult because they can appear in symmetrically
equivalent forms - rotated, mirrored, or inverted. At
first glance, they may look different but still contain the same
chemical bonds.
The team solved this problem by employing 3D Euclidean
neural networks. These advanced computational models
allowed the researchers to identify chemical motifs from simulations
of high-entropy materials with unprecedented detail,
examining them atom-by-atom.
The final task was to quantify the SRO. Freitas used machine
learning to evaluate the different chemical motifs and tag each
with a number. When researchers want to quantify the SRO for
a new material, they run it by the model, which sorts it in its
database and spits out an answer.
The team also invested additional effort in making their
motif identification framework more accessible. " We have
this sheet of all possible permutations of [SRO] already set
up, and we know what number each of them got through this
machine learning process, " Freitas says. " So later, as we run
into simulations, we can sort them out to tell us what that
new SRO will look like. " The neural network easily recognizes
symmetry operations and tags equivalent structures with the
same number.
" If you had to compile all the symmetries yourself, it's a lot
of work. Machine learning organized this for us really quickly
and in a way that was cheap enough that we could apply it in
practice, " Freitas says.
This work was performed by researchers from MIT's Department
of Materials Science and Engineering (DMSE). For more
information, download the Technical Support Package
(free white paper) at mobilityengineeringtech.com/tsp
under the Artificial Intelligence category.
Aerospace & Defense Technology, October 2024
mobilityengineeringtech.com
35
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