The Journal of Physical Chemistry C,
Journal Year:
2024,
Volume and Issue:
128(41), P. 17274 - 17281
Published: Oct. 8, 2024
The
heteroatom-doped
metallic
compounds
supported
on
conductive
substrates
are
excellent
catalysts
for
the
hydrogen
evolution
reaction
(HER)
thanks
to
their
tunable
properties,
e.g.,
and
nonmetallic
compositions,
especially
bimetallic
active
centers
synergistic
effect,
as
well
morphology
interaction
between
substrate.
Only
optimal
combination
these
adjustable
properties
other
external
factors
could
endow
remarkable
HER
catalytic
activity
of
catalysts.
Therefore,
in
this
study,
machine
learning
(ML)
database
based
plenty
from
publicly
available
data
was
conducted
train
three
different
ML
models,
various
features
including
electrolyte
type,
catalyst
morphology,
compositions
(metallic
nonmetallic)
ratios,
additive,
substrate
were
analyzed
figure
out
impacts
overpotential
(OP)
values
determine
outstanding
According
feature
importance
Spearman
coefficient
analysis,
metal
elements
ratio
determined
be
Pt,
Mo
0.5,
heteroatoms
nitrogen,
sulfur,
nickel
foam.
Finally,
model
predicts
that
foam
nickel-supported
composed
Pt
Mo2S3
codoped
with
nitrogen
sulfur
(N,
S-doped
Pt@Mo2S3)
exhibits
admirable
performance
alkaline
electrolytes
a
pretty
low
OP
value
33
mV.
database-guided
provides
an
alternative
rapid
screening
prediction
electrocatalysts.
Journal of Materials Informatics,
Journal Year:
2025,
Volume and Issue:
5(1)
Published: Feb. 12, 2025
Single-atom
catalysts
(SACs)
have
emerged
as
a
research
frontier
in
catalytic
materials,
distinguished
by
their
unique
atom-level
dispersion,
which
significantly
enhances
activity,
selectivity,
and
stability.
SACs
demonstrate
substantial
promise
electrocatalysis
applications,
such
fuel
cells,
CO2
reduction,
hydrogen
production,
due
to
ability
maximize
utilization
of
active
sites.
However,
the
development
efficient
stable
involves
intricate
design
screening
processes.
In
this
work,
artificial
intelligence
(AI),
particularly
machine
learning
(ML)
neural
networks
(NNs),
offers
powerful
tools
for
accelerating
discovery
optimization
SACs.
This
review
systematically
discusses
application
AI
technologies
through
four
key
stages:
(1)
Density
functional
theory
(DFT)
ab
initio
molecular
dynamics
(AIMD)
simulations:
DFT
AIMD
are
used
investigate
mechanisms,
with
high-throughput
applications
expanding
accessible
datasets;
(2)
Regression
models:
ML
regression
models
identify
features
that
influence
performance,
streamlining
selection
promising
materials;
(3)
NNs:
NNs
expedite
known
structural
models,
facilitating
rapid
assessment
potential;
(4)
Generative
adversarial
(GANs):
GANs
enable
prediction
novel
high-performance
tailored
specific
requirements.
work
provides
comprehensive
overview
current
status
insights
recommendations
future
advancements
field.
Journal of Materials Chemistry A,
Journal Year:
2023,
Volume and Issue:
11(46), P. 25410 - 25421
Published: Jan. 1, 2023
Dual-atom
catalysts
(DACs)
have
recently
emerged
as
promising
and
high-activity
for
the
oxygen
reduction
reaction
(ORR),
a
key
process
in
many
electrochemical
energy
conversion
devices.
The Journal of Physical Chemistry Letters,
Journal Year:
2024,
Volume and Issue:
15(4), P. 1121 - 1129
Published: Jan. 24, 2024
Using
first-principles
calculations
combined
with
a
constant-potential
implicit
solvent
model,
we
comprehensively
studied
the
activity
of
oxygen
electrode
reactions
catalyzed
by
electride-supported
FeN4-embedded
graphene
(FeN4Cx).
The
physical
quantities
in
FeN4Cx/electrides,
i.e.,
work
function
electrides,
interlayer
spacing,
stability
heterostructures,
charge
transferred
to
Fe,
d-band
center
and
adsorption
free
energy
O,
are
highly
intercorrelated,
resulting
being
fully
expressed
nature
electrides
themselves,
thereby
achieving
precise
modulation
selecting
different
electrides.
Strikingly,
FeN4PDCx/Ca2N
FeN4PDCx/Y2C
systems
maintain
high
evolution
reaction
(OER)
reduction
(ORR)
overpotential
less
than
0.46
0.62
V
wide
pH
range.
This
provides
an
effective
strategy
for
rational
design
efficient
bifunctional
catalysts
as
well
model
system
simple
activity-descriptor,
helping
realize
significant
advances
devices.
Deleted Journal,
Journal Year:
2024,
Volume and Issue:
1(3), P. 100029 - 100029
Published: July 19, 2024
Electrocatalytic
conversion
of
CO2
into
valuable
products
is
a
promising
approach
toward
mitigating
climate
change
and
energy
crisis.
However,
the
product
diversity
multi-electron
transfer
pathways
drive
development
numerous
strategies
for
catalyst
component
active
site
modifications,
leading
to
long
journey
rational
electrocatalyst
design.
The
integration
machine
learning
(ML)
with
experimental
workload
provides
an
opportunity
speed
up
materials
discovery
by
automatically
exploiting
trends
patterns
from
database.
This
review
focuses
on
interpretability
ML
models
in
design,
demonstrates
reliable
workflow
based
adequate
catalytic
data
refined
descriptors,
satisfactory
configuration
model
appropriate
human
intervention.
Moreover,
combination
data-driven
techs
cutting-edge
methodologies
also
discussed,
which
can
serve
as
bridge
between
contemporary
catalysis
quantum
chemistry.
may
provoke
more
ML-based
innovations
rationalization
design
novel
net-zero
industries.
