Langmuir,
Journal Year:
2023,
Volume and Issue:
39(45), P. 15849 - 15863
Published: Nov. 3, 2023
Metal–organic
frameworks
(MOFs)
have
attracted
tremendous
interest
because
of
their
tunable
structures,
functionalities,
and
physiochemical
properties.
The
nearly
infinite
combinations
metal
nodes
organic
linkers
led
to
the
synthesis
over
100,000
experimental
MOFs
construction
millions
hypothetical
counterparts.
It
is
intractable
identify
best
candidates
in
immense
chemical
space
for
applications
via
conventional
trial-to-error
experiments
or
brute-force
simulations.
Over
past
several
years,
machine
learning
(ML)
has
substantially
transformed
way
MOF
discovery,
design,
synthesis.
Driven
by
abundant
data
from
simulations,
ML
can
not
only
efficiently
accurately
predict
properties
but
also
quantitatively
derive
structure–property
relationships
rational
design
screening.
In
this
Perspective,
we
summarize
recent
achievements
leveraging
aspects
acquisition,
featurization,
model
training,
applications.
Then,
current
challenges
new
opportunities
are
discussed
future
exploration
accelerate
development
vibrant
field.
Coordination Chemistry Reviews,
Journal Year:
2023,
Volume and Issue:
484, P. 215112 - 215112
Published: March 21, 2023
The
reticular
chemistry
of
metal–organic
frameworks
(MOFs)
allows
for
the
generation
an
almost
boundless
number
materials
some
which
can
be
a
substitute
traditionally
used
porous
in
various
fields
including
gas
storage
and
separation,
catalysis,
drug
delivery.
MOFs
their
potential
applications
are
growing
so
quickly
that,
when
novel
synthesized,
testing
them
all
possible
is
not
practical.
High-throughput
computational
screening
approaches
based
on
molecular
simulations
have
been
widely
to
investigate
identify
optimal
specific
application.
Despite
resources,
given
enormous
MOF
material
space,
identification
promising
requires
more
efficient
terms
time
effort.
Leveraging
data-driven
science
techniques
offer
key
benefits
such
as
accelerated
design
discovery
pathways
via
establishment
machine
learning
(ML)
models
interpretation
complex
structure-performance
relationships
that
reach
beyond
expert
intuition.
In
this
review,
we
present
scientific
breakthroughs
propelled
modeling
discuss
state-of-the-art
extending
from
ML
algorithms.
Finally,
provide
our
perspective
opportunities
challenges
future
big
discovery.
Chemical Society Reviews,
Journal Year:
2024,
Volume and Issue:
53(4), P. 2056 - 2098
Published: Jan. 1, 2024
Non-CO
2
greenhouse
gas
mitigation
and
recovery
with
advanced
porous
materials
(MOFs,
COFs,
HOFs,
POPs,
etc.
)
would
significantly
contribute
to
achieving
carbon
neutrality
gain
economic
benefits
concurrently.
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(18)
Published: Jan. 19, 2024
Abstract
Machine
learning
holds
significant
research
potential
in
the
field
of
nanotechnology,
enabling
nanomaterial
structure
and
property
predictions,
facilitating
materials
design
discovery,
reducing
need
for
time‐consuming
labor‐intensive
experiments
simulations.
In
contrast
to
their
achiral
counterparts,
application
machine
chiral
nanomaterials
is
still
its
infancy,
with
a
limited
number
publications
date.
This
despite
great
advance
development
new
sustainable
high
values
optical
activity,
circularly
polarized
luminescence,
enantioselectivity,
as
well
analysis
structural
chirality
by
electron
microscopy.
this
review,
an
methods
used
studying
provided,
subsequently
offering
guidance
on
adapting
extending
work
nanomaterials.
An
overview
within
framework
synthesis–structure–property–application
relationships
presented
insights
how
leverage
study
these
highly
complex
are
provided.
Some
key
recent
reviewed
discussed
Finally,
review
captures
achievements,
ongoing
challenges,
prospective
outlook
very
important
field.
SmartMat,
Journal Year:
2025,
Volume and Issue:
6(1)
Published: Jan. 9, 2025
ABSTRACT
Machine
learning
(ML),
material
genome,
and
big
data
approaches
are
highly
overlapped
in
their
strategies,
algorithms,
models.
They
can
target
various
definitions,
distributions,
correlations
of
concerned
physical
parameters
given
polymer
systems,
have
expanding
applications
as
a
new
paradigm
indispensable
to
conventional
ones.
Their
inherent
advantages
building
quantitative
multivariate
largely
enhanced
the
capability
scientific
understanding
discoveries,
thus
facilitating
mechanism
exploration,
prediction,
high‐throughput
screening,
optimization,
rational
inverse
designs.
This
article
summarizes
representative
progress
recent
two
decades
focusing
on
design,
preparation,
application,
sustainable
development
materials
based
exploration
key
composition–process–structure–property–performance
relationship.
The
integration
both
data‐driven
insights
through
ML
deepen
fundamental
discover
novel
is
categorically
presented.
Despite
construction
application
robust
models,
strategies
algorithms
deal
with
variant
tasks
science
still
rapid
growth.
challenges
prospects
then
We
believe
that
innovation
will
thrive
along
approaches,
from
efficient
design
applications.
Green Energy & Environment,
Journal Year:
2022,
Volume and Issue:
9(1), P. 54 - 70
Published: Dec. 7, 2022
Membrane
technologies
are
becoming
increasingly
versatile
and
helpful
today
for
sustainable
development.
Machine
Learning
(ML),
an
essential
branch
of
artificial
intelligence
(AI),
has
substantially
impacted
the
research
development
norm
new
materials
energy
environment.
This
review
provides
overview
perspectives
on
ML
methodologies
their
applications
in
membrane
design
discovery.
A
brief
is
first
provided
with
current
bottlenecks
potential
solutions.
Through
applications-based
perspective
AI-aided
discovery,
we
further
show
how
strategies
applied
to
discovery
cycle
(including
material
design,
application,
process
knowledge
extraction),
various
systems,
ranging
from
gas,
liquid,
fuel
cell
separation
membranes.
Furthermore,
best
practices
integrating
methods
specific
application
targets
presented
ideal
paradigm
proposed.
The
challenges
be
addressed
prospects
AI
also
highlighted
end.
