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.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(11), P. 7698 - 7707
Published: March 11, 2024
High
entropy
alloys
(HEAs)
are
a
highly
promising
class
of
materials
for
electrocatalysis
as
their
unique
active
site
distributions
break
the
scaling
relations
that
limit
activity
conventional
transition
metal
catalysts.
Existing
Bayesian
optimization
(BO)-based
virtual
screening
approaches
focus
on
catalytic
sole
objective
and
correspondingly
tend
to
identify
unlikely
be
entropically
stabilized.
Here,
we
overcome
this
limitation
with
multiobjective
BO
framework
HEAs
simultaneously
targets
activity,
cost-effectiveness,
entropic
stabilization.
With
diversity-guided
batch
selection
further
boosting
its
data
efficiency,
readily
identifies
numerous
candidates
oxygen
reduction
reaction
strike
balance
between
all
three
objectives
in
hitherto
unchartered
HEA
design
spaces
comprising
up
10
elements.
Chemical Society Reviews,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 1, 2024
This
review
explores
machine
learning's
impact
on
designing
electrocatalysts
for
hydrogen
energy,
detailing
how
it
transcends
traditional
methods
by
utilizing
experimental
and
computational
data
to
enhance
electrocatalyst
efficiency
discovery.
ACS Nano,
Journal Year:
2024,
Volume and Issue:
18(31), P. 20740 - 20750
Published: July 23, 2024
High-entropy
materials
(HEMs)
have
garnered
extensive
attention
owing
to
their
diverse
and
captivating
physicochemical
properties.
Yet,
fine-tuning
morphological
properties
of
HEMs
remains
a
formidable
challenge,
constraining
potential
applications.
To
address
this,
we
present
rapid,
low-energy
consumption
diethylenetriamine
(DETA)-assisted
microwave
hydrothermal
method
for
synthesizing
series
two-dimensional
high-entropy
selenides
(HESes).
Subsequently,
the
obtained
HESes
are
harnessed
photocatalytic
water
splitting.
Noteworthy
is
optimized
HESes,
Cd
Nature Communications,
Journal Year:
2025,
Volume and Issue:
16(1)
Published: Feb. 1, 2025
High-entropy
alloys
(HEAs)
present
a
vast
compositional
space
for
fine-tuning
electrocatalytic
activities,
leveraging
millions
of
distinct
active
sites
on
the
surface.
However,
intricate
local
chemical
environment
poses
challenges
to
rational
and
efficient
design
HEA
electrocatalysts
with
high
reactivity.
Here,
focusing
noble-metal
HEAs
oxygen
reduction
reactions,
we
propose
straightforward
yet
effective
descriptor
quantitively
determining
reactivities
HEAs.
This
is
based
linear
combination
intrinsic
d-band
filling
center
neighborhood
electronegativity.
Our
model
offers
an
accurate
robust
description
binding
strengths
intermediates
different
adsorption
configurations
HEAs,
supported
by
external
density
functional
theory
calculations.
Importantly,
environmental
electronegativity
surface
strongly
related
profile
atom(s)
embedded
within.
Finally,
establish
library
activity
maps
encompassing
nine
elements,
suggesting
that
Pd-rich
Ir-rich
alloys,
such
as
Pd–Ag,
Ir–Pt,
Ir–Au
compositions,
hold
promise
potential
candidates
optimal
electrocatalysts.
High
entropy
promises
enhanced
reaction.
authors
develop
designing
alloy
aid
environments.
The Journal of Physical Chemistry Letters,
Journal Year:
2022,
Volume and Issue:
13(25), P. 5991 - 6002
Published: June 23, 2022
Catalytic
conversion
of
CO2
to
carbon
neutral
fuels
can
be
ecofriendly
and
allow
for
economic
replacement
fossil
fuels.
Here,
we
have
investigated
high-throughput
screening
high
entropy
alloy
(Cu,
Co,
Ni,
Zn,
Sn)
based
catalysts
through
machine
learning
(ML)
hydrogenation
methanol.
Stability
catalytic
activity
studies
these
been
performed
all
possible
combinations,
where
different
elemental,
compositional,
surface
microstructural
features
were
used
as
input
parameters.
Adsorption
energy
values
reduction
intermediates
on
the
CuCoNiZnMg-
CuCoNiZnSn-based
train
ML
models.
Successful
prediction
adsorption
energies
adsorbates
using
CuCoNiZnMg-based
training
data
is
achieved
except
two
intermediates.
Hence,
show
that
selectivity
successfully
predicted
methanol
screened
a
series
entropy-based
(from
36750
considered
catalysts)
which
could
promising
synthesis.
The Chemical Record,
Journal Year:
2022,
Volume and Issue:
22(12)
Published: Sept. 15, 2022
Abstract
Recently,
high‐entropy
alloys
(HEAs)
have
been
extensively
investigated
due
to
their
unique
structural
design,
superior
stability,
excellent
functional
feature
and
mechanical
performance.
However,
most
of
the
reported
HEAs
focus
on
studying
compositional
design
microstructure
properties
materials.
There
are
relatively
few
studies
electrochemical
performance
theoretical
HEAs.
In
addition,
potential
applications
as
energy
storage
materials
for
electrocatalysts
attracted
widely
attention
in
development
application
aspects
electrocatalysis.
It
can
be
attributed
high
conductivity,
stability
electrocatalytic
activities
with
small
overpotential
abundant
active
sites,
which
is
comparable
commercial
noble
metal
catalysts.
this
review,
firstly,
we
briefly
discuss
concept
structure
characteristics
entropy
alloys.
Then,
research
progress
electrocatalysis
also
summarized,
including
hydrogen
evolution
reaction
(HER),
oxygen
(OER)
reduction
(ORR),
respectively.
Finally,
future
trend
prospected
conversion
fields.
