Nano Letters,
Journal Year:
2024,
Volume and Issue:
24(37), P. 11632 - 11640
Published: Sept. 3, 2024
High-entropy
alloys
(HEAs)
present
both
significant
potential
and
challenges
for
developing
efficient
electrocatalysts
due
to
their
diverse
combinations
compositions.
Here,
we
propose
a
procedural
approach
that
combines
high-throughput
experimentation
with
data-driven
strategies
accelerate
the
discovery
of
HEA
hydrogen
evolution
reaction
(HER).
This
enables
rapid
preparation
arrays
various
element
composition
ratios
within
model
system.
The
intrinsic
activity
is
swiftly
screened
using
scanning
electrochemical
cell
microscopy
(SECCM),
providing
precise
composition-activity
data
sets
An
ensemble
machine
learning
(EML)
then
used
predict
database
subspace
Based
on
these
results,
two
groups
promising
catalysts
are
recommended
validated
through
actual
electrocatalytic
evaluations.
approach,
which
strategies,
provides
new
pathway
electrocatalysts.
Advanced Functional Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 12, 2025
Abstract
High‐entropy
materials
(HEMs),
characterized
by
their
unique
compositions
involving
multiple
principal
elements
and
inherent
configurational
disorder,
have
emerged
as
a
focal
point
of
material
science
research
since
introduction,
owing
to
exceptional
structural
stability
superior
performance.
The
distinctive
features
HEMs,
including
the
high‐entropy
effect,
lattice
distortion,
sluggish
diffusion,
cocktail
enabled
wide‐ranging
applications
in
fields
such
energy
storage,
catalysis,
electronic
devices,
beyond.
This
review
systematically
documents
evolution
HEMs
synthesis,
from
traditional
melting‐based
methods
for
bulk
production
recent
breakthroughs
addressing
limitations
elemental
immiscibility,
ultimately
enabling
precise
multi‐path
synthesis
nano‐
sub‐nano
materials.
It
comprehensively
examines
controllable
strategies
across
various
dimensional
scales,
principles
composition‐structure
design,
regulation
multidimensional
morphologies,
multifunctional
properties
materials'
multi‐component
characteristics.
Furthermore,
this
work
prospectively
explores
emerging
that
could
drive
future
development
with
particular
emphasis
on
potential
synergies
between
high‐throughput
experimentation,
data‐driven
approaches,
chiral
factors,
entropy‐driven
strategies,
advanced
high‐resolution
characterization
techniques.
Advanced Functional Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 16, 2025
Abstract
Lithium
dendrite
growth
and
the
resulting
safety
concerns
hinder
application
of
lithium
metal.
Compared
with
single
metal
or
medium
entropy
alloys,
high‐entropy
alloys
(HEAs)
are
a
promising
solution
to
solve
challenges
anodes
due
their
unique
properties.
However,
designing
HEA
layer
appropriate
elements
proportion
has
become
obstacles.
Herein,
machine
learning
(ML),
density
functional
theories
(DFT)
calculation
data
analysis
reveal
contribution
Zn
in
lithiophilicity,
Al
hardness
Fe,
Co,
Ni
providing
magnetism.
The
magnetron
sputtering
is
used
construct
interphase
layer,
three
parameters
(sputtering
power,
time,
substrate
rotation
speed)
optimized
via
particle
swarm
optimization
(PSO)
based
on
logarithm
average
coulombic
efficiency
(CE)
Li||Cu
half
cells.
While
high
strength,
compactness,
flatness
constructed,
Li||Li
symmetric
cell
assembled
by
HEA@Li
at
1
mA
cm
−2
,
mAh
can
cycle
stably
for
2400
h,
discharge
capacity
retention
rate
Li||LFP
>90%
after
300
cycles
C
CE
99.67%.
Design
assisted
ML
provides
path
potential
batteries.
Physica Scripta,
Journal Year:
2025,
Volume and Issue:
100(4), P. 046013 - 046013
Published: March 5, 2025
Abstract
In
recent
years,
the
ideal-
properties
(young’s
modulus,
yield
strength,
toughness)
and
advanced
application
potential
of
high-entropy
alloys
(HEAs)
have
attracted
numerous
researchers.
However,
due
to
their
unique
structure
multiple
structural
combinations,
it
is
challenging
explore
impact
various
factors
on
mechanical
performance
solely
through
experiments.
This
study
considers
concentrations
five
alloy
atoms
working
temperature
as
input
parameters.
Molecular
dynamics
(MD)
simulations
machine
learning
(ML)
algorithms
are
employed
predict
tensile
FeNiCrCoCu
HEAs,
including
Young’s
modulus
(
E
)
toughness
uT
).
A
dataset
1000
HEAs
generated
MD
simulations,
feature
selection
conducted
using
principal
component
analysis
Spearman
correlation
analysis.
XGBoost,
RF,
DT,
LGBoost,
AdaBoost
utilized
comparing
two
methods
prediction
outcomes.
During
ML
model
training,
10-fold
cross-validation
grid
search
obtain
best
models
Root
mean
squard
error
RMSE
),
coefficient
determination
R
2
absolute
MAE
relative
RAE
used
evaluation
metrics.
Results
indicate
that
for
outperforms
analysis,
XGBoost
demonstrates
superior
predictive
compared
other
models.
Predictions
more
accurate
than
those
,
with
exceeding
0.9
four
out
work
may
provide
a
new
method
studying
ML.
future,
this
can
be
applied
research
areas
compositions,
providing
theoretical
support
It
then
further
critical
fields
such
biomedical
aerospace
industries.
