npj Computational Materials,
Journal Year:
2023,
Volume and Issue:
9(1)
Published: Feb. 6, 2023
Abstract
Computational
modeling
of
physical
processes
in
metal-organic
frameworks
(MOFs)
is
highly
challenging
due
to
the
presence
spatial
heterogeneities
and
complex
operating
conditions
which
affect
their
behavior.
Density
functional
theory
(DFT)
may
describe
interatomic
interactions
at
quantum
mechanical
level,
but
computationally
too
expensive
for
systems
beyond
nanometer
picosecond
range.
Herein,
we
propose
an
incremental
learning
scheme
construct
accurate
data-efficient
machine
potentials
MOFs.
The
builds
on
power
equivariant
neural
network
combination
with
parallelized
enhanced
sampling
on-the-fly
training
simultaneously
explore
learn
phase
space
iterative
manner.
With
only
a
few
hundred
single-point
DFT
evaluations
per
material,
transferable
are
obtained,
even
flexible
multiple
structurally
different
phases.
universally
applicable
pave
way
model
framework
materials
larger
spatiotemporal
windows
higher
accuracy.
Journal of Materials Chemistry A,
Journal Year:
2023,
Volume and Issue:
11(18), P. 9721 - 9747
Published: Jan. 1, 2023
Metal–organic
frameworks
(MOFs)
have
become
popular
precursors
for
the
construction
of
porous
carbon
nanomaterials
(CNMs)
with
inherited
characteristics
and
advantages,
showing
great
potential
in
environment
energy
applications.
Green Energy & Environment,
Journal Year:
2023,
Volume and Issue:
9(2), P. 217 - 310
Published: Jan. 3, 2023
Carbon
peaking
and
carbon
neutralization
trigger
a
technical
revolution
in
energy
&
environment
related
fields.
Development
of
new
technologies
for
green
production
storage,
industrial
saving
efficiency
reinforcement,
capture,
pollutant
gas
treatment
is
highly
imperious
demand.
The
emerging
porous
framework
materials
such
as
metal–organic
frameworks
(MOFs),
covalent
organic
(COFs)
hydrogen-bonded
(HOFs),
owing
to
the
permanent
porosity,
tremendous
specific
surface
area,
designable
structure
customizable
functionality,
have
shown
great
potential
major
energy-consuming
processes,
including
sustainable
catalytic
conversion,
energy-efficient
separation
storage.
Herein,
this
manuscript
presents
systematic
review
global
comprehensive
applications,
from
macroscopic
application
perspective.
Journal of Materials Chemistry A,
Journal Year:
2022,
Volume and Issue:
10(9), P. 4653 - 4659
Published: Jan. 1, 2022
A
3D
cobalt
porphyrin-based
covalent
organic
framework,
3D-Por(Co/H)-COF,
was
prepared
to
maximize
the
accessibility
of
active
sites
for
enhanced
activity
electrochemical
CO
2
reduction
reaction.
npj Computational Materials,
Journal Year:
2023,
Volume and Issue:
9(1)
Published: Feb. 6, 2023
Abstract
Computational
modeling
of
physical
processes
in
metal-organic
frameworks
(MOFs)
is
highly
challenging
due
to
the
presence
spatial
heterogeneities
and
complex
operating
conditions
which
affect
their
behavior.
Density
functional
theory
(DFT)
may
describe
interatomic
interactions
at
quantum
mechanical
level,
but
computationally
too
expensive
for
systems
beyond
nanometer
picosecond
range.
Herein,
we
propose
an
incremental
learning
scheme
construct
accurate
data-efficient
machine
potentials
MOFs.
The
builds
on
power
equivariant
neural
network
combination
with
parallelized
enhanced
sampling
on-the-fly
training
simultaneously
explore
learn
phase
space
iterative
manner.
With
only
a
few
hundred
single-point
DFT
evaluations
per
material,
transferable
are
obtained,
even
flexible
multiple
structurally
different
phases.
universally
applicable
pave
way
model
framework
materials
larger
spatiotemporal
windows
higher
accuracy.
