Advanced Materials Technologies,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 25, 2025
Abstract
With
technological
advancement
and
development,
there
is
a
tremendous
increase
in
demand
for
different
smart
materials
because
of
their
stimulation
from
external
sources.
Moreover,
the
time‐dependent
response
provides
insight
into
fabrication
these
using
4D
printing
(4DP)
techniques.
Hence,
this
study
presents
comprehensive
review
4DP
materials.
The
covers
aspects
material,
design
optimization
to
printing.
Herein,
have
been
discussed
detail
based
on
physical,
biological,
chemical
stimuli‐responsive
subtype's
behavior.
For
designing
materials,
usage
tools
such
as
new
software,
finite
element
analysis,
machine
learning
are
also
discussed.
challenging
responsive
natures
complexity
mechanisms.
detailed
present
3D
techniques,
use
4DP,
how
future
applications
can
be
incorporated
with
material
presented.
help
learning,
directions
fabricating
4DP.
challenges
utilization
comprehensively
covered.
Advanced Science,
Journal Year:
2024,
Volume and Issue:
11(13)
Published: Jan. 26, 2024
Abstract
The
availability
of
an
ever‐expanding
portfolio
2D
materials
with
rich
internal
degrees
freedom
(spin,
excitonic,
valley,
sublattice,
and
layer
pseudospin)
together
the
unique
ability
to
tailor
heterostructures
made
by
in
a
precisely
chosen
stacking
sequence
relative
crystallographic
alignments,
offers
unprecedented
platform
for
realizing
design.
However,
breadth
multi‐dimensional
parameter
space
massive
data
sets
involved
is
emblematic
complex,
resource‐intensive
experimentation,
which
not
only
challenges
current
state
art
but
also
renders
exhaustive
sampling
untenable.
To
this
end,
machine
learning,
very
powerful
data‐driven
approach
subset
artificial
intelligence,
potential
game‐changer,
enabling
cheaper
–
yet
more
efficient
alternative
traditional
computational
strategies.
It
new
paradigm
autonomous
experimentation
accelerated
discovery
machine‐assisted
design
functional
heterostructures.
Here,
study
reviews
recent
progress
such
endeavors,
highlight
various
emerging
opportunities
frontier
research
area.
Nature Communications,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: May 21, 2024
Abstract
Energy
absorbing
efficiency
is
a
key
determinant
of
structure’s
ability
to
provide
mechanical
protection
and
defined
by
the
amount
energy
that
can
be
absorbed
prior
stresses
increasing
level
damages
system
protected.
Here,
we
explore
additively
manufactured
polymer
structures
using
self-driving
lab
(SDL)
perform
>25,000
physical
experiments
on
generalized
cylindrical
shells.
We
use
human-SDL
collaborative
approach
where
are
selected
from
over
trillions
candidates
in
an
11-dimensional
parameter
space
Bayesian
optimization
then
automatically
performed
while
human
team
monitors
progress
periodically
modify
aspects
system.
The
result
this
campaign
discovery
structure
with
75.2%
library
experimental
data
reveals
transferable
principles
for
designing
tough
structures.
Journal of Applied Mechanics,
Journal Year:
2023,
Volume and Issue:
91(3)
Published: Oct. 5, 2023
Abstract
3D/4D
printing
offers
significant
flexibility
in
manufacturing
complex
structures
with
a
diverse
range
of
mechanical
responses,
while
also
posing
critical
needs
tackling
challenging
inverse
design
problems.
The
rapidly
developing
machine
learning
(ML)
approach
new
opportunities
and
has
attracted
interest
the
field.
In
this
perspective
paper,
we
highlight
recent
advancements
utilizing
ML
for
designing
printed
desired
responses.
First,
provide
an
overview
common
forward
problems,
relevant
types
structures,
space
responses
printing.
Second,
review
works
that
have
employed
variety
approaches
different
ranging
from
structural
properties
to
active
shape
changes.
Finally,
briefly
discuss
main
challenges,
summarize
existing
potential
approaches,
extend
discussion
broader
problems
field
This
paper
is
expected
foundational
guides
insights
into
application
design.
International Journal for Numerical Methods in Engineering,
Journal Year:
2024,
Volume and Issue:
125(14)
Published: April 5, 2024
Abstract
Machine
learning
(ML)
and
Deep
(DL)
are
increasingly
pivotal
in
the
design
of
advanced
metamaterials,
seamlessly
integrated
with
material
or
topology
optimization.
Their
intrinsic
capability
to
predict
interconnect
properties
across
vast
spaces,
often
computationally
prohibitive
for
conventional
methods,
has
led
groundbreaking
possibilities.
This
paper
introduces
an
innovative
machine
approach
optimization
acoustic
focusing
on
Multiresonant
Layered
Acoustic
Metamaterial
(MLAM),
designed
targeted
noise
attenuation
at
low
frequencies
(below
1000
Hz).
method
leverages
ML
create
a
continuous
model
Representative
Volume
Element
(RVE)
effective
essential
evaluating
sound
transmission
loss
(STL),
subsequently
used
optimize
overall
configuration
maximum
using
Genetic
Algorithm
(GA).
The
significance
this
methodology
lies
its
ability
deliver
rapid
results
without
compromising
accuracy,
significantly
reducing
computational
overhead
complete
by
several
orders
magnitude.
To
demonstrate
versatility
scalability
approach,
it
is
extended
more
intricate
RVE
model,
characterized
higher
number
parameters,
optimized
same
strategy.
In
addition,
underscore
potential
techniques
synergy
traditional
optimization,
comparative
analysis
conducted,
comparing
outcomes
proposed
those
obtained
through
direct
numerical
simulation
(DNS)
corresponding
full
3D
MLAM
model.
highlights
transformative
combination,
particularly
when
addressing
complex
topological
challenges
significant
demands,
ushering
new
era
metamaterial
component
design.
Chemical Physics Reviews,
Journal Year:
2025,
Volume and Issue:
6(1)
Published: March 1, 2025
Surfaces
and
interfaces
play
key
roles
in
chemical
material
science.
Understanding
physical
processes
at
complex
surfaces
is
a
challenging
task.
Machine
learning
provides
powerful
tool
to
help
analyze
accelerate
simulations.
This
comprehensive
review
affords
an
overview
of
the
applications
machine
study
systems
materials.
We
categorize
into
following
broad
categories:
solid–solid
interface,
solid–liquid
liquid–liquid
surface
solid,
liquid,
three-phase
interfaces.
High-throughput
screening,
combined
first-principles
calculations,
force
field
accelerated
molecular
dynamics
simulations
are
used
rational
design
such
as
all-solid-state
batteries,
solar
cells,
heterogeneous
catalysis.
detailed
information
on
for
Applied Sciences,
Journal Year:
2024,
Volume and Issue:
14(15), P. 6590 - 6590
Published: July 28, 2024
The
importance
of
biomaterials
lies
in
their
fundamental
roles
medical
applications
such
as
tissue
engineering,
drug
delivery,
implantable
devices,
and
radiological
phantoms,
with
interactions
biological
systems
being
critically
important.
In
recent
years,
advancements
deep
learning
(DL),
artificial
intelligence
(AI),
machine
(ML),
supervised
(SL),
unsupervised
(UL),
reinforcement
(RL)
have
significantly
transformed
the
field
biomaterials.
These
technologies
introduced
new
possibilities
for
design,
optimization,
predictive
modeling
This
review
explores
DL
AI
biomaterial
development,
emphasizing
optimizing
material
properties,
advancing
innovative
design
processes,
accurately
predicting
behaviors.
We
examine
integration
enhancing
performance
functional
attributes
biomaterials,
explore
AI-driven
methodologies
creation
novel
assess
capabilities
ML
responses
to
various
environmental
stimuli.
Our
aim
is
elucidate
pivotal
contributions
DL,
AI,
science
potential
drive
innovation
development
superior
It
suggested
that
future
research
should
further
deepen
these
technologies’
application
areas.
