Advanced Functional Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 18, 2025
Abstract
Porous
carbon
materials
(PCMs)
have
long
played
key
roles
in
energy
storage
and
conversion
fields,
known
for
their
abundant
raw
materials,
tunable
pore
structures,
large
surface
area,
excellent
conductivity.
Despite
significant
progress,
there
remains
a
substantial
gap
between
the
precise
design
of
PCMs
full
utilization
unique
properties
developing
high‐performance
electrode
materials.
Herein,
this
review
systematically
comprehensively
introduces
from
traditional
synthesis,
machine
learning‐assisted
principles
to
applications.
Specifically,
preparation
methods
microporous,
mesoporous,
macroporous,
hierarchically
porous
are
thoroughly
summarized,
with
an
emphasis
on
structural
control
rules
formation
mechanisms.
It
also
highlights
advantages
alkali
metal‐ion
batteries,
metal–sulfur
supercapacitors,
electrocatalysis.
Insights
situ
operando
characterizations
provide
deep
understanding
correlation
structure
performance.
Finally,
current
challenges
future
directions
discussed,
emphasizing
need
further
advancements
meet
evolving
demands.
This
offers
valuable
guidance
rational
points
out
research
development.
Advanced Materials,
Journal Year:
2023,
Volume and Issue:
35(23)
Published: March 17, 2023
The
development
of
a
data-driven
science
paradigm
is
greatly
revolutionizing
the
process
materials
discovery.
Particularly,
exploring
novel
nonlinear
optical
(NLO)
with
birefringent
phase-matching
ability
to
deep-ultraviolet
(UV)
region
vital
significance
for
field
laser
technologies.
Herein,
target-driven
design
framework
combining
high-throughput
calculations
(HTC),
crystal
structure
prediction,
and
interpretable
machine
learning
(ML)
proposed
accelerate
discovery
deep-UV
NLO
materials.
Using
dataset
generated
from
HTC,
an
ML
regression
model
predicting
birefringence
developed
first
time,
which
exhibits
possibility
achieving
fast
accurate
prediction.
Essentially,
structures
are
adopted
as
only
known
input
this
establish
close
structure-property
relationship
mapping
birefringence.
Utilizing
ML-predicted
can
affect
shortest
wavelength,
full
list
potential
chemical
compositions
based
on
efficient
screening
strategy
identified.
Further,
eight
good
stability
discovered
show
applications
in
region,
owing
their
promising
NLO-related
properties.
This
study
provides
new
insight
into
identify
desired
high
performances
broad
space
at
low
computational
cost.
Chemical Reviews,
Journal Year:
2024,
Volume and Issue:
124(7), P. 4258 - 4331
Published: March 28, 2024
Artificial
Intelligence
(AI)
has
advanced
material
research
that
were
previously
intractable,
for
example,
the
machine
learning
(ML)
been
able
to
predict
some
unprecedented
thermal
properties.
In
this
review,
we
first
elucidate
methodologies
underpinning
discriminative
and
generative
models,
as
well
paradigm
of
optimization
approaches.
Then,
present
a
series
case
studies
showcasing
application
in
metamaterial
design.
Finally,
give
brief
discussion
on
challenges
opportunities
fast
developing
field.
particular,
review
provides:
(1)
Optimization
metamaterials
using
algorithms
achieve
specific
target
(2)
Integration
models
with
enhance
computational
efficiency.
(3)
Generative
structural
design
metamaterials.
Chemical Reviews,
Journal Year:
2024,
Volume and Issue:
124(22), P. 12738 - 12843
Published: Nov. 5, 2024
The
quest
to
imbue
machines
with
intelligence
akin
that
of
humans,
through
the
development
adaptable
neuromorphic
devices
and
creation
artificial
neural
systems,
has
long
stood
as
a
pivotal
goal
in
both
scientific
inquiry
industrial
advancement.
Recent
advancements
flexible
electronics
primarily
rely
on
nanomaterials
polymers
owing
their
inherent
uniformity,
superior
mechanical
electrical
capabilities,
versatile
functionalities.
However,
this
field
is
still
its
nascent
stage,
necessitating
continuous
efforts
materials
innovation
device/system
design.
Therefore,
it
imperative
conduct
an
extensive
comprehensive
analysis
summarize
current
progress.
This
review
highlights
applications
neuromorphics,
involving
inorganic
(zero-/one-/two-dimensional,
heterostructure),
carbon-based
such
carbon
nanotubes
(CNTs)
graphene,
polymers.
Additionally,
comparison
summary
structural
compositions,
design
strategies,
key
performance,
significant
these
are
provided.
Furthermore,
challenges
future
directions
pertaining
materials/devices/systems
associated
neuromorphics
also
addressed.
aim
shed
light
rapidly
growing
attract
experts
from
diverse
disciplines
(e.g.,
electronics,
science,
neurobiology),
foster
further
for
accelerated
development.
Advanced Materials,
Journal Year:
2023,
Volume and Issue:
36(6)
Published: Oct. 10, 2023
Abstract
Combining
materials
science,
artificial
intelligence
(AI),
physical
chemistry,
and
other
disciplines,
informatics
is
continuously
accelerating
the
vigorous
development
of
new
materials.
The
emergence
“GPT
(Generative
Pre‐trained
Transformer)
AI”
shows
that
scientific
research
field
has
entered
era
intelligent
civilization
with
“data”
as
basic
factor
“algorithm
+
computing
power”
core
productivity.
continuous
innovation
AI
will
impact
cognitive
laws
methods,
reconstruct
knowledge
wisdom
system.
This
leads
to
think
more
about
informatics.
Here,
a
comprehensive
discussion
models
infrastructures
provided,
advances
in
discovery
design
are
reviewed.
With
rise
paradigms
triggered
by
“AI
for
Science”,
vane
informatics:
“MatGPT”,
proposed
technical
path
planning
from
aspects
data,
descriptors,
generative
models,
pretraining
directed
collaborative
training,
experimental
robots,
well
efforts
preparations
needed
develop
generation
informatics,
carried
out.
Finally,
challenges
constraints
faced
discussed,
order
achieve
digital,
intelligent,
automated
construction
joint
interdisciplinary
scientists.
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(37)
Published: Feb. 29, 2024
Abstract
Human–machine
interaction
(HMI)
technology
has
undergone
significant
advancements
in
recent
years,
enabling
seamless
communication
between
humans
and
machines.
Its
expansion
extended
into
various
emerging
domains,
including
human
healthcare,
machine
perception,
biointerfaces,
thereby
magnifying
the
demand
for
advanced
intelligent
technologies.
Neuromorphic
computing,
a
paradigm
rooted
nanoionic
devices
that
emulate
operations
architecture
of
brain,
emerged
as
powerful
tool
highly
efficient
information
processing.
This
paper
delivers
comprehensive
review
developments
device‐based
neuromorphic
computing
technologies
their
pivotal
role
shaping
next‐generation
HMI.
Through
detailed
examination
fundamental
mechanisms
behaviors,
explores
ability
memristors
ion‐gated
transistors
to
intricate
functions
neurons
synapses.
Crucial
performance
metrics,
such
reliability,
energy
efficiency,
flexibility,
biocompatibility,
are
rigorously
evaluated.
Potential
applications,
challenges,
opportunities
using
HMI
technologies,
discussed
outlooked,
shedding
light
on
fusion
with
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(30)
Published: May 25, 2024
Abstract
Computational
chemistry
is
an
indispensable
tool
for
understanding
molecules
and
predicting
chemical
properties.
However,
traditional
computational
methods
face
significant
challenges
due
to
the
difficulty
of
solving
Schrödinger
equations
increasing
cost
with
size
molecular
system.
In
response,
there
has
been
a
surge
interest
in
leveraging
artificial
intelligence
(AI)
machine
learning
(ML)
techniques
silico
experiments.
Integrating
AI
ML
into
increases
scalability
speed
exploration
space.
remain,
particularly
regarding
reproducibility
transferability
models.
This
review
highlights
evolution
from,
complementing,
or
replacing
energy
property
predictions.
Starting
from
models
trained
entirely
on
numerical
data,
journey
set
forth
toward
ideal
model
incorporating
physical
laws
quantum
mechanics.
paper
also
reviews
existing
their
intertwining,
outlines
roadmap
future
research,
identifies
areas
improvement
innovation.
Ultimately,
goal
develop
architectures
capable
accurate
transferable
solutions
equation,
thereby
revolutionizing
experiments
within
materials
science.
Medicine,
Journal Year:
2024,
Volume and Issue:
103(27), P. e38811 - e38811
Published: July 5, 2024
The
application
of
artificial
intelligence
(AI)
technologies
in
scientific
research
has
significantly
enhanced
efficiency
and
accuracy
but
also
introduced
new
forms
academic
misconduct,
such
as
data
fabrication
text
plagiarism
using
AI
algorithms.
These
practices
jeopardize
integrity
can
mislead
directions.
This
study
addresses
these
challenges,
underscoring
the
need
for
community
to
strengthen
ethical
norms,
enhance
researcher
qualifications,
establish
rigorous
review
mechanisms.
To
ensure
responsible
transparent
processes,
we
recommend
following
specific
key
actions:
Development
enforcement
comprehensive
guidelines
that
include
clear
protocols
use
analysis
publication,
ensuring
transparency
accountability
AI-assisted
research.
Implementation
mandatory
ethics
training
researchers,
aimed
at
fostering
an
in-depth
understanding
potential
misuses
promoting
practices.
Establishment
international
collaboration
frameworks
facilitate
exchange
best
development
unified
standards
Protecting
is
paramount
maintaining
public
trust
science,
making
recommendations
urgent
consideration
action.
Atmosphere,
Journal Year:
2024,
Volume and Issue:
15(6), P. 689 - 689
Published: June 6, 2024
Accurate
and
rapid
weather
forecasting
climate
modeling
are
universal
goals
in
human
development.
While
Numerical
Weather
Prediction
(NWP)
remains
the
gold
standard,
it
faces
challenges
like
inherent
atmospheric
uncertainties
computational
costs,
especially
post-Moore
era.
With
advent
of
deep
learning,
field
has
been
revolutionized
through
data-driven
models.
This
paper
reviews
key
models
significant
developments
modeling.
It
provides
an
overview
these
models,
covering
aspects
such
as
dataset
selection,
model
design,
training
process,
acceleration,
prediction
effectiveness.
Data-driven
trained
on
reanalysis
data
can
provide
effective
forecasts
with
accuracy
(ACC)
greater
than
0.6
for
up
to
15
days
at
a
spatial
resolution
0.25°.
These
outperform
or
match
most
advanced
NWP
methods
90%
variables,
reducing
forecast
generation
time
from
hours
seconds.
reliably
simulate
patterns
decades
100
years,
offering
magnitude
savings
competitive
performance.
Despite
their
advantages,
have
limitations,
including
poor
interpretability,
evaluating
uncertainty,
conservative
predictions
extreme
cases.
Future
research
should
focus
larger
integrating
more
physical
constraints,
enhancing
evaluation
methods.
