Interdisciplinary materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 15, 2025
ABSTRACT
Data‐driven
artificial
intelligence
provides
strong
technical
support
for
addressing
global
energy
and
environmental
issues.
The
powerful
data
processing
analysis
capabilities
of
machine
learning
(ML)
can
quickly
predict
electrocatalytic
performance,
improving
the
efficiency
catalyst
design
time‐consuming
inefficient
nature
traditional
design.
By
integrating
ML
with
theoretical
calculations
experiments,
catalytic
reaction
processes
be
precisely
regulated.
This
not
only
accelerates
discovery
new
catalysts
but
also
drives
development
more
efficient
environmentally
friendly
sustainable
technologies.
In
this
article,
we
discuss
approaches
to
discovering
novel
driven
by
ML,
focusing
on
activity
prediction,
barrier
optimization,
innovative
materials.
We
systematically
application
in
field
electrocatalysis
explore
future
prospects
domain.
provide
a
comprehensive
in‐depth
its
potential
development.
Chemistry,
Год журнала:
2025,
Номер
7(3), С. 80 - 80
Опубликована: Май 13, 2025
This
review
focuses
on
research
machine
learning-enabled
two-dimensional
(2D)
materials,
exploring
the
progress
and
prospects
of
this
interdisciplinary
field.
At
a
fundamental
level,
learning
algorithms
incorporate
imaging
systems
to
build
highly
accurate
viewing
frameworks
for
material
analysis.
Two-dimensional
materials
have
rich
set
optical
properties,
including
light
absorption
emission,
anisotropy,
photoluminescence,
nonlinear
effects,
which
can
accurately
understand
through
image
characterization,
spectral
fusion,
quantitative
Meanwhile,
preparation
process
post-processing
are
key
aspects
in
growth
regulation
2D
helps
optimize
experiments
by
analyzing
kinetics
fine
control.
Related
has
spawned
many
academic
achievements,
gradually
penetrating
electronics,
energy,
other
industrial
applications.
The
innovation
technology
deepening
multidisciplinary
integration
expected
unlock
more
emerging
applications
expand
application
boundaries
materials.
Processes,
Год журнала:
2025,
Номер
13(5), С. 1506 - 1506
Опубликована: Май 14, 2025
Hydrogen
is
a
key
energy
carrier,
playing
vital
role
in
sustainable
systems.
This
review
provides
comparative
analysis
of
physical,
chemical,
and
innovative
hydrogen
storage
methods
from
technical,
environmental,
economic
perspectives.
It
has
been
identified
that
compressed
liquefied
are
predominantly
utilized
transportation
applications,
while
chemical
transport
mainly
supported
by
liquid
organic
carriers
(LOHC)
ammonia-based
Although
metal
hydrides
nanomaterials
offer
high
capacities,
they
face
limitations
related
to
cost
thermal
management.
Furthermore,
artificial
intelligence
(AI)-
machine
learning
(ML)-based
optimization
techniques
highlighted
for
their
potential
enhance
efficiency
improve
system
performance.
In
conclusion,
systems
achieve
broader
applicability,
it
recommended
integrated
approaches
be
adopted—focusing
on
material
development,
feasibility,
environmental
sustainability.
Interdisciplinary materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 15, 2025
ABSTRACT
Data‐driven
artificial
intelligence
provides
strong
technical
support
for
addressing
global
energy
and
environmental
issues.
The
powerful
data
processing
analysis
capabilities
of
machine
learning
(ML)
can
quickly
predict
electrocatalytic
performance,
improving
the
efficiency
catalyst
design
time‐consuming
inefficient
nature
traditional
design.
By
integrating
ML
with
theoretical
calculations
experiments,
catalytic
reaction
processes
be
precisely
regulated.
This
not
only
accelerates
discovery
new
catalysts
but
also
drives
development
more
efficient
environmentally
friendly
sustainable
technologies.
In
this
article,
we
discuss
approaches
to
discovering
novel
driven
by
ML,
focusing
on
activity
prediction,
barrier
optimization,
innovative
materials.
We
systematically
application
in
field
electrocatalysis
explore
future
prospects
domain.
provide
a
comprehensive
in‐depth
its
potential
development.
