RSC Advances,
Journal Year:
2024,
Volume and Issue:
14(33), P. 23672 - 23682
Published: Jan. 1, 2024
Finding
novel
energetic
materials
with
good
comprehensive
performance
has
always
been
challenging
because
of
the
low
efficiency
in
conventional
trial
and
error
experimental
procedure.
In
this
paper,
we
established
a
deep
learning
model
high
prediction
accuracy
using
embedded
features
Directed
Message
Passing
Neural
Networks.
The
combined
high-throughput
screening
was
shown
to
facilitate
rapid
discovery
fused
[5,5]
biheterocyclic
energy
excellent
thermal
stability.
Density
Functional
Theory
(DFT)
calculations
proved
that
performances
targeting
molecules
are
consistent
predicted
results
from
model.
Furthermore,
6,7-trinitro-3H-pyrrolo[1,2-b][1,2,4]triazo-5-amine
both
detonation
properties
stability
screened
out,
whose
crystal
structure
intermolecular
interactions
were
also
analyzed.
Water,
Journal Year:
2024,
Volume and Issue:
16(10), P. 1407 - 1407
Published: May 15, 2024
Artificial
intelligence
has
undergone
rapid
development
in
the
last
thirty
years
and
been
widely
used
fields
of
materials,
new
energy,
medicine,
engineering.
Similarly,
a
growing
area
research
is
use
deep
learning
(DL)
methods
connection
with
hydrological
time
series
to
better
comprehend
expose
changing
rules
these
series.
Consequently,
we
provide
review
latest
advancements
employing
DL
techniques
for
forecasting.
First,
examine
application
convolutional
neural
networks
(CNNs)
recurrent
(RNNs)
forecasting,
along
comparison
between
them.
Second,
made
basic
enhanced
long
short-term
memory
(LSTM)
analyzing
their
improvements,
prediction
accuracies,
computational
costs.
Third,
performance
GRUs,
other
models
including
generative
adversarial
(GANs),
residual
(ResNets),
graph
(GNNs),
estimated
Finally,
this
paper
discusses
benefits
challenges
associated
forecasting
using
techniques,
CNN,
RNN,
LSTM,
GAN,
ResNet,
GNN
models.
Additionally,
it
outlines
key
issues
that
need
be
addressed
future.
Energy and AI,
Journal Year:
2023,
Volume and Issue:
15, P. 100317 - 100317
Published: Nov. 9, 2023
Ceramic
electrochemical
cells
(CECs)
are
promising
devices
for
clean
and
efficient
energy
conversion
storage
due
to
their
high
efficiency,
more
extended
system
durability,
less
expensive
materials.
However,
the
search
suitable
materials
with
desired
properties,
including
ionic
electronic
conductivity,
thermal
stability,
chemical
compatibility,
presents
ongoing
challenges
that
impede
widespread
adoption
further
advancement
in
field.
Artificial
intelligence
(AI)
has
emerged
as
a
versatile
tool
capable
of
enhancing
expediting
discovery
cycle
CECs
through
data-driven
modeling,
simulation,
optimization
techniques.
Herein,
we
comprehensively
review
state-of-the-art
AI
applications
design
CECs,
covering
various
material
aspects,
database
construction,
data
pre-processing,
methods.
We
also
present
some
representative
case
studies
AI-predicted
synthesized
provide
insightful
highlights
about
approaches.
emphasize
main
implications
contributions
approach
advancing
CEC
technology,
such
reducing
trial-and-error
experiments,
exploring
vast
space,
discovering
novel
optimal
materials,
understanding
materials-performance
relationships.
discuss
approach's
limitations
future
directions
addressing
model
challenges,
improving
extending
models
methods,
integrating
other
computational
experimental
conclude
by
suggesting
potential
collaborations
CECs.
Propellants Explosives Pyrotechnics,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 14, 2025
ABSTRACT
A
prediction
tool
for
the
burning
rate
of
composite
solid
propellants
using
an
artificial
neural
network
(ANN)‐based
model
is
proposed.
The
methodology
adopted
can
be
divided
into
two
parts
(a)
estimation
interaction
between
process
variables
Spearman
rank‐order
correlation
method
and
(b)
building
ANN‐based
to
predict
from
a
trimmed
dataset
consisting
significant
variables.
multilayer
perceptron
(MLP)
was
fed
with
as
input,
backpropagation
algorithm
used
solve
mathematical
in
Python.
ANN
hyperparameters
tuning
carried
out
Grid
Search
It
found
that
average
motor
high
accuracy
when
compared
obtained
ballistic
evaluation
test
motors.
This
helps
propellant
composition
mechanical
physical
properties
without
firing
motors
(BEM).
Advances in chemical and materials engineering book series,
Journal Year:
2024,
Volume and Issue:
unknown, P. 389 - 458
Published: Nov. 29, 2024
Lifestyle
choices
significantly
shape
the
intricate
chemical
makeup
of
human
body,
impacting
health
and
lifespan.
The
use
bio-chemical
adsorbents
in
multistage
potable
water
filtration
represents
a
major
breakthrough
addressing
global
quality
challenges.
fundamentals
are
discussed,
distinguishing
between
bio-based
materials
highlighting
various
types
such
as
activated
carbon,
biochar,
graphene
oxide.
concept
systems
is
introduced
by
advantages
over
single-stage
key
design
considerations.
chapter
then
delves
into
applications
purification,
presenting
case
studies
comparative
analyses
with
traditional
methods
to
demonstrate
their
efficacy
performance
metrics.
Strategies
enhance
adsorption
efficiency
through
surface
modifications,
hybrid
systems,
composite
explored
detail
targets
on
environmental
impact,
sustainability,
regulatory
standards,
future
trends
adsorbents.
Molecules,
Journal Year:
2023,
Volume and Issue:
28(4), P. 1900 - 1900
Published: Feb. 16, 2023
Artificial
intelligence
technology
shows
the
advantages
of
improving
efficiency,
reducing
costs,
shortening
time,
number
staff
on
site
and
achieving
precise
operations,
making
impressive
research
progress
in
fields
drug
discovery
development,
but
there
are
few
reports
application
energetic
materials.
This
paper
addresses
high
safety
risks
current
nitrification
process
materials,
comprehensively
analyses
summarizes
main
their
control
elements
process,
proposes
possibilities
suggestions
for
using
artificial
to
enhance
“essential
safety”
reviews
field
synthesis,
looks
forward
prospects
materials
provides
support
guidance
safe
processing
propellants
explosives
industry.
Journal of Applied Physics,
Journal Year:
2023,
Volume and Issue:
134(16)
Published: Oct. 23, 2023
Reactive
burn
models
for
heterogeneous
energetic
materials
(EMs)
must
account
chemistry
as
well
microstructure
to
predict
shock-to-detonation
transition
(SDT).
Upon
shock
loading,
the
collapse
of
individual
voids
leads
ignition
hotspots,
which
then
grow
and
interact
consume
surrounding
material.
The
sub-grid
dynamics
shock-void
interactions
hotspot
development
are
transmitted
macro-scale
SDT
calculations
in
form
a
global
reactive
“burn
model.”
This
paper
presents
physically
evocative
model,
called
meso-informed
source
terms
energy
localization
(MISSEL),
close
governing
equations
calculating
SDT.
model
parameters
explicitly
related
four
measurable
physical
quantities:
two
depending
on
(the
porosity
ϕ
average
pore
size
D¯void),
one
shock–microstructure
interaction
fraction
critical
ξcr),
other
front
velocity
Vhs).
These
quantities
individually
quantifiable
using
small
number
rather
inexpensive
meso-scale
simulations.
As
constructed,
overcomes
following
problems
that
hinder
models:
(1)
opacity
more
sophisticated
surrogate/machine-learning
approaches
bridging
meso-
macro-scales,
(2)
large
high-resolution
mesoscale
simulations
necessary
train
machine-learning
algorithms,
(3)
need
calibration
many
free
appear
phenomenological
models.
is
tested
against
experimental
data
James
curves
specific
class
pressed
1,3,5,7-tetranitro-1,3,5,7-tetrazoctane
materials.
simple,
evocative,
fast-to-construct
MISSEL
suggests
route
develop
frameworks
physics-informed,
simulation-derived
Energies,
Journal Year:
2024,
Volume and Issue:
17(17), P. 4333 - 4333
Published: Aug. 29, 2024
Most
nanothermite
compositions
utilise
Al
as
a
fuel,
due
to
its
low
cost,
high
reactivity
and
availability.
Nevertheless,
aluminothermites
exhibit
ignition
temperature
active
metal
content.
In
this
paper,
the
combustion
behaviour
of
Ti/CuO
Ti/CuO/NC
systems
is
discussed.
The
were
prepared
with
wet-mixing/sonication
process
followed
by
an
electrospray
technique
examined
in
terms
their
mechanical
radiation
sensitivity,
energetic
parameters
morphology.
results
exhibited
strong
correlation
between
equivalence
ratio
parameters.
performed
tests
showed
crucial
impact
addiction
chosen
binder
on
morphology
performance
compositions.
our
experiments
indicate
occurrence
different
mechanism
than
one
observed
for
Al-based
nanothermites.
case,
involves
limitation
diffusion
oxidising
agent
decomposition
products
into
reactive
fuel
core.
İnsan ve Sosyal Bilimler Dergisi,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 28, 2024
Artificial
intelligence
(AI),
as
the
pioneer
of
today's
technological
advances,
brings
innovation
to
many
sectors
and
graphic
design
is
among
these
sectors.
Within
rapidly
developing
technology
our
age,
integration
AI
technologies
into
field
provides
a
significant
acceleration
in
processes.
In
this
context,
it
predicted
that
use
contributes
accelerate
processes,
increase
efficiency
improve
user
experience
interactive
design.
Additionally,
research
examines
current
potential
status.
The
study
adopts
qualitative
methods
comparative
analysis
logical
reasoning
limited
reviewed
literature
studies
reviewed.The
findings
show
AI-assisted
tools
enable
more
creative
solutions.
results
AI-supported
Propellants Explosives Pyrotechnics,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 23, 2024
Abstract
Data
science
and
artificial
intelligence
are
playing
an
increasingly
important
role
in
the
physical
sciences.
Unfortunately,
field
of
energetic
materials
data
scarcity
limits
accuracy
even
applicability
ML
tools.
To
address
limitations,
we
compiled
multi‐modal
data:
both
experimental
computational
results
for
several
properties.
We
find
that
multi‐task
neural
networks
can
learn
from
outperform
single‐task
models
trained
specific
As
expected,
improvement
is
more
significant
data‐scarce
These
using
descriptors
built
simple
molecular
information
be
readily
applied
large‐scale
screening
to
explore
multiple
properties
simultaneously.
This
approach
widely
applicable
fields
outside
materials.