Langmuir,
Год журнала:
2023,
Номер
39(45), С. 15849 - 15863
Опубликована: Ноя. 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.
Abstract
For
gas
separation
and
catalysis
by
metal‐organic
frameworks
(MOFs),
diffusion
has
a
substantial
impact
on
the
process'
overall
rate,
so
it
is
necessary
to
determine
molecular
behavior
within
MOFs.
In
this
study,
an
interpretable
machine
learing
(ML)
model,
light
gradient
boosting
(LGBM),
trained
predict
diffusivity
selectivity
of
9
gases
(Kr,
Xe,
CH
4
,
N
2
H
S,
O
CO
He).
these
gases,
LGBM
displays
high
accuracy
(average
R
=
0.962)
superior
extrapolation
for
C
6
.
And
model
calculation
five
orders
magnitude
faster
than
dynamics
(MD)
simulations.
Subsequently,
using
interactive
desktop
application
developed
that
can
help
researchers
quickly
accurately
calculate
molecules
in
porous
crystal
materials.
Finally,
authors
find
difference
polarizability
(
ΔPol
)
key
factor
governing
combining
with
Shapley
additive
explanation
(SHAP).
By
ML,
optimal
MOFs
are
selected
separating
binary
mixtures
methanation.
This
work
provides
new
direction
exploring
structure‐property
relationships
realizing
rapid
diffusivity.
ACS Applied Materials & Interfaces,
Год журнала:
2023,
Номер
15(13), С. 17421 - 17431
Опубликована: Март 27, 2023
Considering
the
existence
of
a
large
number
and
variety
metal-organic
frameworks
(MOFs)
ionic
liquids
(ILs),
assessing
gas
separation
potential
all
possible
IL/MOF
composites
by
purely
experimental
methods
is
not
practical.
In
this
work,
we
combined
molecular
simulations
machine
learning
(ML)
algorithms
to
computationally
design
an
composite.
Molecular
were
first
performed
screen
approximately
1000
different
1-n-butyl-3-methylimidazolium
tetrafluoroborate
([BMIM][BF4])
with
MOFs
for
CO2
N2
adsorption.
The
results
used
develop
ML
models
that
can
accurately
predict
adsorption
performances
[BMIM][BF4]/MOF
composites.
most
important
features
affect
CO2/N2
selectivity
extracted
from
utilized
generate
composite,
[BMIM][BF4]/UiO-66,
which
was
present
in
original
material
data
set.
This
composite
finally
synthesized,
characterized,
tested
separation.
Experimentally
measured
[BMIM][BF4]/UiO-66
matched
well
predicted
model,
it
found
be
comparable,
if
higher
than
previously
synthesized
reported
literature.
Our
proposed
approach
combining
will
highly
useful
any
within
seconds
compared
extensive
time
effort
requirements
methods.
Journal of Materials Chemistry A,
Год журнала:
2023,
Номер
11(29), С. 15600 - 15634
Опубликована: Янв. 1, 2023
The
Troger's
base
(TB)
polymer
has
been
considered
as
promising
CO
2
separation
membrane
materials
and
have
intensively
studied.
In
the
current
work,
progress
of
TB
polymeric
membranes
for
is
summarized
analyzed.
Materials Today Energy,
Год журнала:
2023,
Номер
38, С. 101426 - 101426
Опубликована: Сен. 23, 2023
Hydrogen
(H2)
is
a
promising
energy
carrier
for
achieving
net
zero
carbon
emissions.
Metal
organic
frameworks
(MOFs)
and
covalent
(COFs)
have
emerged
as
strong
alternatives
to
traditional
porous
materials
highly
efficient
H2
storage
purification
applications.
With
the
very
rapid
continuous
increase
in
number
variety
of
MOFs
COFs,
early
studies
this
field
focused
on
experimental
testing
few
types
randomly
selected
recently
evolved
into
combining
computational
screening
large
material
databases
with
machine
learning
(ML).
In
review,
we
highlighted
recent
trends
merging
molecular
modeling
ML
COFs
purification.
After
reviewing
high-throughput
aiming
determine
best
candidates
adsorption
separation,
discussed
that
use
extracting
hidden
structure-performance
relations
from
simulation
results
provide
new
guidelines
inverse
design
novel
MOFs.
Finally,
addressed
current
opportunities
challenges
fusing
data
science
speed
development
innovative
adsorbent
membrane
respectively.
ACS Nano,
Год журнала:
2024,
Номер
18(35), С. 23842 - 23875
Опубликована: Авг. 22, 2024
Machine
learning
(ML)
using
data
sets
of
atomic
and
molecular
force
fields
(FFs)
has
made
significant
progress
provided
benefits
in
the
chemistry
material
science.
This
work
examines
interactions
between
materials
computational
science
at
scales
for
metal-organic
framework
(MOF)
adsorbent
development
toward
carbon
dioxide
(CO
Chemical Society Reviews,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 25, 2024
The
design
and
synthesis
of
MOFs
have
evolved
from
traditional
large-scale
approaches
to
function-oriented
modifications,
recently
AI
predictions,
which
save
time,
reduce
costs,
enhance
the
efficiency
achieving
target
functions.
Journal of Membrane Science,
Год журнала:
2024,
Номер
713, С. 123256 - 123256
Опубликована: Сен. 3, 2024
Machine
learning
(ML)
has
been
rapidly
transforming
the
landscape
of
natural
sciences
and
potential
to
revolutionize
process
data
analysis
hypothesis
formulation
as
well
expand
scientific
knowledge.
ML
particularly
instrumental
in
advancement
cheminformatics
materials
science,
including
membrane
technology.
In
this
review,
we
analyze
current
state-of-the-art
membrane-related
applications
from
perspectives.
We
first
discuss
foundations
different
algorithms
design
choices.
Then,
traditional
deep
methods,
application
examples
literature,
are
reported.
also
importance
both
molecular
membrane-system
featurization.
Moreover,
follow
up
on
discussion
with
science
detail
literature
using
data-driven
methods
property
prediction
fabrication.
Various
fields
discussed,
such
reverse
osmosis,
gas
separation,
nanofiltration.
differentiate
between
downstream
predictive
tasks
generative
design.
Additionally,
formulate
best
practices
minimum
requirements
for
reporting
reproducible
studies
field
membranes.
This
is
systematic
comprehensive
review
science.
