The Journal of Physical Chemistry C,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Feb. 5, 2025
In
this
study,
the
adsorption
mechanism
of
water
in
metal–organic
framework
NU-1000
was
investigated
using
molecular
simulations.
The
simulations
predict
a
significant
impact
small
changes
terminal
aquo
ligand
orientation
on
shape
and
pressure
condensation
step
isotherm.
analysis
revealed
that
rotational
mobility
ligands,
often
neglected
computational
studies,
can
shift
by
up
to
20%
relative
humidity
scale.
By
examining
modes
interaction
sites,
it
demonstrated
configurational
Zr6O8
node
affect
significantly
change
nature
interactions
from
hydrophobic
hydrophilic.
We
propose
robust
approach
account
for
these
simulations,
achieving
good
agreement
with
experimental
results.
This
work
underscores
necessity
considering
local,
flexibility
avoid
mischaracterization
MOFs'
properties.
Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(24)
Published: March 12, 2024
Abstract
Modern
human
civilization
deeply
relies
on
the
rapid
advancement
of
cutting‐edge
electronic
systems
that
have
revolutionized
communication,
education,
aviation,
and
entertainment.
However,
electromagnetic
interference
(EMI)
generated
by
digital
poses
a
significant
threat
to
society,
potentially
leading
future
crisis.
While
numerous
efforts
are
made
develop
nanotechnological
shielding
mitigate
detrimental
effects
EMI,
there
is
limited
focus
creating
absorption‐dominant
solutions.
Achieving
EMI
shields
requires
careful
structural
design
engineering,
starting
from
smallest
components
considering
most
effective
wave
attenuating
factors.
This
review
offers
comprehensive
overview
structures,
emphasizing
critical
elements
design,
mechanisms,
limitations
both
traditional
shields,
common
misconceptions
about
foundational
principles
science.
systematic
serves
as
scientific
guide
for
designing
structures
prioritize
absorption,
highlighting
an
often‐overlooked
aspect
Chemical Society Reviews,
Journal Year:
2024,
Volume and Issue:
53(14), P. 7328 - 7362
Published: Jan. 1, 2024
This
Tutorial
Review
on
atmospheric
water
harvesting
evaluates
sorbents’
essential
mechanisms
and
design
principles,
focusing
chemical
material
system-level
strategies
to
enhance
production
efficiency
address
global
scarcity.
Digital Discovery,
Journal Year:
2024,
Volume and Issue:
3(3), P. 491 - 501
Published: Jan. 1, 2024
The
integration
of
artificial
intelligence
into
scientific
research
opens
new
avenues
with
the
advent
GPT-4V,
a
large
language
model
equipped
vision
capabilities.
Chemical Science,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 9, 2024
Large
language
models
(LLMs)
have
emerged
as
powerful
tools
in
chemistry,
significantly
impacting
molecule
design,
property
prediction,
and
synthesis
optimization.
This
review
highlights
LLM
capabilities
these
domains
their
potential
to
accelerate
scientific
discovery
through
automation.
We
also
LLM-based
autonomous
agents:
LLMs
with
a
broader
set
of
interact
surrounding
environment.
These
agents
perform
diverse
tasks
such
paper
scraping,
interfacing
automated
laboratories,
planning.
As
are
an
emerging
topic,
we
extend
the
scope
our
beyond
chemistry
discuss
across
any
domains.
covers
recent
history,
current
capabilities,
design
agents,
addressing
specific
challenges,
opportunities,
future
directions
chemistry.
Key
challenges
include
data
quality
integration,
model
interpretability,
need
for
standard
benchmarks,
while
point
towards
more
sophisticated
multi-modal
enhanced
collaboration
between
experimental
methods.
Due
quick
pace
this
field,
repository
has
been
built
keep
track
latest
studies:
https://github.com/ur-whitelab/LLMs-in-science.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(29), P. 19654 - 19659
Published: July 11, 2024
We
evaluate
the
effectiveness
of
pretrained
and
fine-tuned
large
language
models
(LLMs)
for
predicting
synthesizability
inorganic
compounds
selection
precursors
needed
to
perform
synthesis.
The
predictions
LLMs
are
comparable
to─and
sometimes
better
than─recent
bespoke
machine
learning
these
tasks
but
require
only
minimal
user
expertise,
cost,
time
develop.
Therefore,
this
strategy
can
serve
both
as
an
effective
strong
baseline
future
studies
various
chemical
applications
a
practical
tool
experimental
chemists.
Industrial & Engineering Chemistry Research,
Journal Year:
2025,
Volume and Issue:
64(9), P. 4637 - 4668
Published: Feb. 24, 2025
This
review
discusses
the
transformative
impact
of
convergence
artificial
intelligence
(AI)
and
laboratory
automation
on
discovery
synthesis
metal–organic
frameworks
(MOFs).
MOFs,
known
for
their
tunable
structures
extensive
applications
in
fields
such
as
energy
storage,
drug
delivery,
environmental
remediation,
pose
significant
challenges
due
to
complex
processes
high
structural
diversity.
Laboratory
has
streamlined
repetitive
tasks,
enabled
high-throughput
screening
reaction
conditions,
accelerated
optimization
protocols.
The
integration
AI,
particularly
Transformers
large
language
models
(LLMs),
further
revolutionized
MOF
research
by
analyzing
massive
data
sets,
predicting
material
properties,
guiding
experimental
design.
emergence
self-driving
laboratories
(SDLs),
where
AI-driven
decision-making
is
coupled
with
automated
experimentation,
represents
next
frontier
research.
While
remain
fully
realizing
potential
this
synergistic
approach,
AI
heralds
a
new
era
efficiency
innovation
engineering
materials.
