The Future of Catalysis: Applying Graph Neural Networks for Intelligent Catalyst Design
Wiley Interdisciplinary Reviews Computational Molecular Science,
Год журнала:
2025,
Номер
15(2)
Опубликована: Март 1, 2025
ABSTRACT
With
the
increasing
global
demand
for
energy
transition
and
environmental
sustainability,
catalysts
play
a
vital
role
in
mitigating
climate
change,
as
they
facilitate
over
90%
of
chemical
material
conversions.
It
is
important
to
investigate
complex
structures
properties
enhanced
performance,
which
artificial
intelligence
(AI)
methods,
especially
graph
neural
networks
(GNNs)
could
be
useful.
In
this
article,
we
explore
cutting‐edge
applications
future
potential
GNNs
intelligent
catalyst
design.
The
fundamental
theories
their
practical
catalytic
simulation
inverse
design
are
first
reviewed.
We
analyze
critical
roles
accelerating
screening,
performance
prediction,
reaction
pathway
analysis,
mechanism
modeling.
By
leveraging
convolution
techniques
accurately
represent
molecular
structures,
integrating
symmetry
constraints
ensure
physical
consistency,
applying
generative
models
efficiently
space,
these
approaches
work
synergistically
enhance
efficiency
accuracy
Furthermore,
highlight
high‐quality
databases
crucial
catalysis
research
innovative
application
thermocatalysis,
electrocatalysis,
photocatalysis,
biocatalysis.
end,
key
directions
advancing
catalysis:
dynamic
frameworks
real‐time
conditions,
hierarchical
linking
atomic
details
features,
multi‐task
interpretability
mechanisms
reveal
pathways.
believe
advancements
will
significantly
broaden
science,
paving
way
more
efficient,
accurate,
sustainable
methodologies.
Язык: Английский
Active Learning‐Driven Discovery of Donor‐Acceptor Covalent Triazine Frameworks for High‐Performance Photocatalysts
Advanced Functional Materials,
Год журнала:
2025,
Номер
unknown
Опубликована: Май 2, 2025
Abstract
Donor‐acceptor
(D‐A)
structure
enables
precise
tuning
of
the
electronic
and
optical
properties
materials,
enabling
widely
applicable
in
organic
semiconductors
photocatalysts.
However,
vast
diversity
donor
acceptor
units
their
combinations
pose
considerable
challenges
to
experimental
development.
Here,
this
study
presents
a
screening
strategy
that
integrates
an
active
learning
(AL)‐based
multi‐model
framework
with
synthesis
validation
discover
high‐performance
D‐A
covalent
triazine
frameworks
(CTFs)
This
combines
AL
model,
trained
on
data
reported
D‐A‐CTFs,
graph
neural
networks
model
establishes
relationship
between
molecular
properties.
Meanwhile,
expert
chemical
knowledge
is
incorporated
into
improve
synthesizability
stability,
resulting
113
identified
candidates
from
database
21807
structures.
Experimental
confirms
9
out
10
newly
synthesized
D‐A‐CTFs
exhibit
predicted
photocatalytic
performances.
Notably,
CTF‐[1,1′‐Biphenyl]‐4,4′‐dicarbaldehyde
achieved
record
hydrogen
evolution
rate
33.29
mmol
g
−1
h
for
CTF‐based
bulk
Further
feature
engineering
analysis
reveals
carbon
nitrogen
charges
critically
determine
performance,
offering
optimization
design.
paves
promising
way
accelerate
discovery
effective
structured
materials.
Язык: Английский
Interpretable machine learning for stability and electronic structure prediction of Janus III–VI van der Waals heterostructures
Materials Genome Engineering Advances,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 4, 2024
Abstract
Machine
learning
(ML)
techniques
have
made
enormous
progress
in
the
field
of
materials
science.
However,
many
conventional
ML
algorithms
operate
as
“black‐boxes”,
lacking
transparency
revealing
explicit
relationships
between
material
features
and
target
properties.
To
address
this,
development
interpretable
models
is
essential
to
drive
further
advancements
AI‐driven
discovery.
In
this
study,
we
present
an
framework
that
combines
traditional
machine
with
symbolic
regression,
using
Janus
III–VI
vdW
heterostructures
a
case
study.
This
approach
enables
fast
accurate
predictions
stability
electronic
structure.
Our
results
demonstrate
prediction
accuracy
classification
model
for
stability,
based
on
formation
energy,
reaches
0.960.
On
other
hand,
R
2
,
MAE,
RMSE
value
regression
structure
prediction,
band
gap,
achieves
0.927,
0.113,
0.141
testing
set,
respectively.
Additionally,
identify
universal
descriptor
comprising
five
simple
parameters
reveals
underlying
physical
candidate
their
gaps.
not
only
delivers
high
gap
but
also
provides
insight
into
Язык: Английский
Photochemical N-Formylation of Amines and Cyclic Carbonate Synthesis from Epoxides by the Use of Light-Mediated Fixation of Carbon Dioxide Using Covalent Organic Framework/g-C3N4 Composites
Industrial & Engineering Chemistry Research,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 2, 2024
A
proficient
heterogeneous
catalytic
system
for
the
photocatalytic
N-formylation
reaction
of
amines
and
cyclic
carbonate
synthesis
from
epoxides
using
CO2
as
a
carbon
source
under
ambient
conditions
has
been
documented.
sophisticated
approach
established
current
process,
aiming
successful
production
formamides
carbonates
with
high
levels
selectivity
efficiency
by
adjusting
several
variables
such
solvent,
time,
well
light
involved
in
reaction.
We
have
synthesized
two
distinct
catalysts,
T-COF
N-COF,
along
g-C3N4
heterojunction,
demonstrating
outstanding
performance.
Compared
to
g-C3N4@N-COF,
g-C3N4@T-COF
photocatalyst
showed
significantly
better
light-driven
formation
other
suitable
at
room
temperature.
The
g-C3N4@COF
photocatalysts
can
be
recycled
used
multiple
times
without
any
noticeable
decrease
efficiency.
Язык: Английский