Physical Chemistry Chemical Physics,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
Carbon
nitride
research
has
reached
a
promising
point
in
today's
endeavours
with
diverse
applications
including
photocatalysis,
energy
storage,
and
sensing
due
to
their
unique
electronic
structural
properties.
Recent
advances
machine
learning
(ML)
have
opened
new
avenues
for
exploring
optimizing
the
potential
of
these
materials.
This
study
presents
comprehensive
review
integration
ML
techniques
carbon
an
introduction
CN
classifications
recent
advancements.
We
discuss
methodologies
employed,
such
as
supervised
learning,
unsupervised
reinforcement
predicting
material
properties,
synthesis
conditions,
enhancing
performance
metrics.
Key
findings
indicate
that
algorithms
can
significantly
reduce
experimental
trial-and-error,
accelerate
discovery
processes,
provide
deeper
insights
into
structure-property
relationships
nitride.
The
synergistic
effect
combining
traditional
approaches
is
highlighted,
showcasing
studies
where
driven
models
successfully
predicted
novel
compositions
enhanced
functional
Future
directions
this
field
are
also
proposed,
emphasizing
need
high-quality
datasets,
advanced
models,
interdisciplinary
collaborations
fully
realize
materials
next-generation
technologies.
Nanomaterials,
Год журнала:
2025,
Номер
15(3), С. 183 - 183
Опубликована: Янв. 24, 2025
Mustard
gas
(HD)
is
a
well-known
chemical
warfare
agent,
recognized
for
its
extreme
toxicity
and
severe
hazards.
Metal–organic
frameworks
(MOFs),
with
their
unique
structural
properties,
show
significant
potential
HD
adsorption
applications.
Due
to
the
hazards
of
HD,
most
experimental
studies
focus
on
simulants,
but
molecular
simulation
research
these
simulants
remains
limited.
Simulation
analyses
can
uncover
structure–performance
relationships
enable
validation,
optimizing
methods,
improving
material
design
performance
predictions.
This
study
integrates
simulations,
machine
learning
(ML),
fingerprinting
(MFs)
identify
MOFs
high
simulant
diethyl
sulfide
(DES),
followed
by
in-depth
analysis
comparison.
First,
are
categorized
into
Top,
Middle,
Bottom
materials
based
efficiency.
Univariate
analysis,
learning,
then
used
compare
distinguishing
features
fingerprints
each
category.
helps
optimal
ranges
Top
materials,
providing
reference
initial
screening.
Machine
feature
importance
combined
SHAP
identifies
key
that
significantly
influence
model
predictions
across
categories,
offering
valuable
insights
future
design.
Molecular
fingerprint
reveals
critical
combinations,
showing
optimized
when
such
as
metal
oxides,
nitrogen-containing
heterocycles,
six-membered
rings,
C=C
double
bonds
co-exist.
The
integrated
using
HTCS,
ML,
MFs
provides
new
perspectives
designing
high-performance
demonstrates
developing
CWAs
simulants.
Journal of Chemical Theory and Computation,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 28, 2025
Two-dimensional
(2D)
nanomaterials
are
at
the
forefront
of
potential
technological
advancements.
Carbon-based
materials
have
been
extensively
studied
since
synthesizing
graphene,
which
revealed
properties
great
interest
for
novel
applications
across
diverse
scientific
and
domains.
New
carbon
allotropes
continue
to
be
explored
theoretically,
with
several
successful
synthesis
processes
carbon-based
recently
achieved.
In
this
context,
study
investigates
mechanical
thermal
DHQ-based
monolayers
nanotubes,
a
allotrope
characterized
by
4-,
6-,
10-membered
rings,
route
using
naphthalene
as
molecular
precursor.
A
machine-learned
interatomic
(MLIP)
was
developed
explore
nanomaterial's
behavior
larger
scales
than
those
accessible
through
first-principles
calculations.
The
MLIP
trained
on
data
derived
from
DFT/PBE
(density
functional
theory/Perdew–Burke–Ernzerhof)
level
ab
initio
dynamics
(AIMD).
Classical
(CMD)
simulations,
employing
MLIP,
that
Young's
modulus
nanotubes
ranges
127
243
N/m,
depending
chirality
diameter,
fracture
occurring
strains
between
13.6
17.4%
initial
length.
Regarding
response,
critical
temperature
2200
K
identified,
marking
onset
transition
an
amorphous
phase
higher
temperatures.
APL Machine Learning,
Год журнала:
2025,
Номер
3(1)
Опубликована: Март 1, 2025
Traditional
transistors
based
on
complementary
metal–oxide–semiconductor
and
field-effect
are
facing
significant
limitations
as
device
scaling
reaches
the
limits
of
Moore’s
law.
These
include
increased
leakage
currents,
pronounced
short-channel
effects,
quantum
tunneling
through
gate
oxide,
leading
to
higher
power
consumption
deviations
from
ideal
behavior.
Tunnel
Field-Effect
Transistors
(TFETs)
can
overcome
these
challenges
by
utilizing
charge
carriers
switch
between
off
states
achieve
a
subthreshold
swing
below
60
mV/decade.
This
allows
for
lower
consumption,
continued
scaling,
improved
performance
in
low-power
applications.
review
focuses
design
operation
TFETs,
emphasizing
optimization
material
selection
advanced
simulation
techniques.
The
discussion
will
specifically
address
use
two-dimensional
materials
TFET
explore
methods
ranging
multi-scale
approaches
machine
learning-driven
optimization.
Applied Physics Reviews,
Год журнала:
2025,
Номер
12(1)
Опубликована: Март 1, 2025
This
is
a
review
of
theoretical
and
methodological
development
over
the
past
decade
pertaining
to
computational
characterization
thermoelectric
materials
from
first
principles.
Primary
focus
on
electronic
thermal
transport
in
solids.
Particular
attention
given
relationships
between
various
methods
terms
hierarchy
as
well
tradeoff
physical
accuracy
efficiency
each.
Further
covered
are
up-and-coming
for
modeling
defect
formation
dopability,
keys
realizing
material's
potential.
We
present
discuss
all
these
close
connection
with
parallel
developments
high-throughput
infrastructure
code
implementation
that
enable
large-scale
computing
screening.
In
all,
it
demonstrated
advances
tools
now
ripe
efficient
accurate
targeting
needles
haystack,
which
“next-generation”
materials.
