Phase Stability and Transitions in High-Entropy Alloys: Insights from Lattice Gas Models, Computational Simulations, and Experimental Validation
Entropy,
Journal Year:
2025,
Volume and Issue:
27(5), P. 464 - 464
Published: April 25, 2025
High-entropy
alloys
(HEAs)
are
a
novel
class
of
metallic
materials
composed
five
or
more
principal
elements
in
near-equimolar
ratios.
This
unconventional
composition
leads
to
high
configurational
entropy,
which
promotes
the
formation
solid
solution
phases
with
enhanced
mechanical
properties,
thermal
stability,
and
corrosion
resistance.
Phase
stability
plays
critical
role
determining
their
structural
integrity
performance.
study
provides
focused
review
HEA
phase
transitions,
emphasizing
lattice
gas
models
predicting
behavior.
By
integrating
statistical
mechanics
thermodynamic
principles,
enable
accurate
modeling
atomic
interactions,
segregation,
order-disorder
transformations.
The
combination
computational
simulations
(e.g.,
Monte
Carlo,
molecular
dynamics)
experimental
validation
XRD,
TEM,
APT)
improves
predictive
accuracy.
Furthermore,
advances
data-driven
methodologies
facilitate
high-throughput
exploration
compositions,
accelerating
discovery
optimized
superior
Beyond
applications,
HEAs
demonstrate
potential
functional
domains,
such
as
catalysis,
hydrogen
storage,
energy
technologies.
brings
together
theoretical
modeling—particularly
approaches—and
form
unified
understanding
behavior
high-entropy
alloys.
highlighting
mechanisms
behind
transitions
implications
for
material
performance,
this
work
aims
support
design
optimization
real-world
applications
aerospace,
systems,
engineering.
Language: Английский
Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow
Journal of Chemical Information and Modeling,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 23, 2024
Machine
learning
(ML)
methods
provide
a
pathway
to
accurately
predict
molecular
properties,
leveraging
patterns
derived
from
structure–property
relationships
within
materials
databases.
This
approach
holds
significant
importance
in
drug
discovery
and
design,
where
the
rapid,
efficient
screening
of
molecules
can
accelerate
development
new
pharmaceuticals
chemical
for
highly
specialized
target
application.
Unsupervised
self-supervised
applied
graph-based
or
geometric
models
have
garnered
considerable
traction.
More
recently,
transformer-based
language
emerged
as
powerful
tools.
Nevertheless,
their
application
entails
computational
resources,
owing
need
an
extensive
pretraining
process
on
vast
corpus
unlabeled
data
sets.
To
this
end,
we
present
semisupervised
strategy
that
harnesses
substructure
vector
embeddings
conjunction
with
ML-based
feature
selection
workflow
various
properties.
We
evaluate
efficacy
our
modeling
methodology
across
diverse
range
sets,
encompassing
both
regression
classification
tasks.
Our
findings
demonstrate
superior
performance
compared
most
existing
state-of-the-art
algorithms,
while
offering
advantages
terms
balancing
model
accuracy
requirements.
Moreover,
provides
deeper
insights
into
interactions
are
essential
interpretability.
A
case
study
is
conducted
lipophilicity
molecules,
exemplifying
robustness
strategy.
The
result
underscores
meticulous
analysis
over
mere
reliance
predictive
high
degree
algorithmic
complexity.
Language: Английский