npj Climate and Atmospheric Science,
Journal Year:
2024,
Volume and Issue:
7(1)
Published: Aug. 3, 2024
Abstract
Increasing
heatwave
intensity
and
mortality
demand
timely
accurate
prediction.
The
present
study
focused
on
teleconnection,
the
influence
of
distant
land
ocean
variability
local
weather
events,
to
drive
long-term
predictions.
complexity
teleconnection
poses
challenges
for
physical-based
prediction
models.
In
this
study,
we
employed
a
machine
learning
model
explainable
artificial
intelligence
identify
drivers
heatwaves
in
South
Korea.
Drivers
were
selected
based
their
statistical
significance
with
annual
frequency
(
|
R
>
0.3,
p
<
0.05).
Our
analysis
revealed
that
two
snow
depth
(SD)
variabilities—a
decrease
Gobi
Desert
increase
Tianshan
Mountains—are
most
important
predictive
drivers.
These
exhibit
high
correlation
summer
climate
conditions
conducive
heatwaves.
lays
groundwork
further
research
into
understanding
land–atmosphere
interactions
over
these
SD
regions
significant
impact
patterns
Industrial & Engineering Chemistry Research,
Journal Year:
2024,
Volume and Issue:
63(33), P. 14727 - 14747
Published: Aug. 7, 2024
CO2
methanation
represents
a
promising
technological
pathway
for
achieving
efficient
carbon
dioxide
resource
utilization
and
mitigation
of
greenhouse
gas
emissions.
However,
the
development
catalysts
with
high
activity
at
low
temperatures
(<250
°C)
remains
formidable
challenge.
To
address
time-consuming
costly
nature
traditional
catalyst
methods,
this
study
proposes
an
interpretable
machine
learning
(ML)-assisted
reverse
design
framework
catalysts.
This
integrates
advantages
ML,
interpretability
analysis,
multiobjective
optimization
methods
to
elucidate
intricate
interplay
among
compositions,
preparation
conditions,
reaction
parameters,
activity.
A
data
set
containing
2777
points
is
established
construct
various
ML
models.
After
fine-tuning
key
hyperparameters
four
models,
comprehensive
comparison
conducted
evaluate
their
predictive
performance.
The
light
gradient
boosting
(LGBM)
model
demonstrates
superior
accuracy,
attributed
its
minimal
toot
mean
squared
error
less
than
0.31
highest
R2
value
surpassing
0.90.
An
analysis
ascertain
most
significant
features
impact
on
outputs
optimal
LGBM
using
postvalidation
interpretation
methods.
It
indicates
that
appropriately
reducing
active
component
content,
first
support
calcination
temperature,
inert
content
are
favorable
reaction.
Finally,
coupled
NGSA-III
algorithm
maximize
conversion
ratio
CH4
selectivity
in
reactions.
Three
Ru-
three
Ni-based
new
have
been
successfully
predicted
recommended
temperatures.
In
particular,
optimized
Ru–Ba/Cr2O3–SrO
higher
97.04%
72.22%
Crystal Growth & Design,
Journal Year:
2023,
Volume and Issue:
23(8), P. 5705 - 5718
Published: July 18, 2023
Natural
gas
purification
and
biogas
recovery
require
efficient
separation
of
CO2
from
CH4,
as
CH4
is
increasingly
being
recognized
a
promising
substitute
for
petroleum
due
to
its
environmentally
sustainable
nature,
abundance
in
natural
resources,
economic
benefits.
In
the
present
work,
3D
Cd-based
metal–organic
framework,
[Cd2(DBrTPA)2(DMF)3]
(MUT-11)
2,5-[dibromoterephthalic
acid
(DBrTPA)
dimethyl
formamide
(DMF)]
was
synthesized
using
combination
different
synthetic
methods
fully
characterized
via
several
techniques.
Additionally,
variety
organic
solvents
were
employed
perform
solvent
stability
test.
The
MUT-11
structure
subjected
Grand
Canonical
Monte
Carlo
molecular
dynamics
simulations
study
adsorption
characteristics
gases
both
pure
binary
states.
results
acquired
through
simulation-based
analysis
revealed
that
dominant
all
pressure
temperature
conditions.
Nanomaterials,
Journal Year:
2025,
Volume and Issue:
15(3), P. 183 - 183
Published: Jan. 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.
