The
growing
need
for
fast
and
reliable
energy
delivery
in
various
applications
ranging
from
electric
vehicles
portable
electronics
to
grid-scale
storage
demands
high-performance
systems
capable
of
operating
at
high
charge/discharge
rates
(C-rates).
Aqueous
zinc-ion
batteries
(AZIBs)
offer
a
promising
alternative
conventional
lithium-ion
primarily
due
their
inherent
safety,
environmental
friendliness,
low
cost,
theoretical
capacity.
Quinone-based
cathodes,
with
redox
kinetics
capacities,
are
particularly
suitable
high-rate
applications.
However,
practical
application
AZIBs
is
limited
by
solubility
aqueous
electrolytes,
leading
significant
capacity
fading
poor
long-term
cycling
stability,
especially
elevated
C-rates.
To
address
these
challenges,
this
study
investigates
the
use
Nafion
membranes
as
ion-selective
barriers
stabilize
quinone
cathodes
prevent
dissolution
active
materials.
evaluates
four
quinone-based
cathodes─2,3,5,6-tetrachloro-1,4-benzoquinone
(TCBQ),
1,4-naphthoquinone
(NQ),
anthraquinone
(AQ),
poly(2-chloro-3,5,6-trisulfide-1,4-benzoquinone)
(PCTBQ)─in
AZIBs,
focusing
on
effect
membrane
conditioning
1
M
ZnSO4
electrolyte.
results
demonstrate
that
optimized
significantly
enhances
stability
performance
reducing
dissolution,
improving
cyclability,
maintaining
stable
retention
under
conditions,
i.e.,
35C.
These
findings
emphasize
importance
its
potential
advance
development
durable,
rapid
Patterns,
Journal Year:
2023,
Volume and Issue:
4(2), P. 100678 - 100678
Published: Feb. 1, 2023
Molecular
discovery
is
a
multi-objective
optimization
problem
that
requires
identifying
molecule
or
set
of
molecules
balance
multiple,
often
competing,
properties.
Multi-objective
molecular
design
commonly
addressed
by
combining
properties
interest
into
single
objective
function
using
scalarization,
which
imposes
assumptions
about
relative
importance
and
uncovers
little
the
trade-offs
between
objectives.
In
contrast
to
Pareto
does
not
require
knowledge
reveals
However,
it
introduces
additional
considerations
in
algorithm
design.
this
review,
we
describe
pool-based
de
novo
generative
approaches
with
focus
on
algorithms.
We
show
how
relatively
direct
extension
Bayesian
plethora
different
models
extend
from
single-objective
similar
ways
non-dominated
sorting
reward
(reinforcement
learning)
select
for
retraining
(distribution
propagation
(genetic
algorithms).
Finally,
discuss
some
remaining
challenges
opportunities
field,
emphasizing
opportunity
adopt
techniques
Chemical Reviews,
Journal Year:
2024,
Volume and Issue:
124(16), P. 9633 - 9732
Published: Aug. 13, 2024
Self-driving
laboratories
(SDLs)
promise
an
accelerated
application
of
the
scientific
method.
Through
automation
experimental
workflows,
along
with
autonomous
planning,
SDLs
hold
potential
to
greatly
accelerate
research
in
chemistry
and
materials
discovery.
This
review
provides
in-depth
analysis
state-of-the-art
SDL
technology,
its
applications
across
various
disciplines,
implications
for
industry.
additionally
overview
enabling
technologies
SDLs,
including
their
hardware,
software,
integration
laboratory
infrastructure.
Most
importantly,
this
explores
diverse
range
domains
where
have
made
significant
contributions,
from
drug
discovery
science
genomics
chemistry.
We
provide
a
comprehensive
existing
real-world
examples
different
levels
automation,
challenges
limitations
associated
each
domain.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 10, 2024
Rapid
advancements
in
artificial
intelligence
(AI)
have
enabled
breakthroughs
across
many
scientific
disciplines.
In
organic
chemistry,
the
challenge
of
planning
complex
multistep
chemical
syntheses
should
conceptually
be
well-suited
for
AI.
Yet,
development
AI
synthesis
planners
trained
solely
on
reaction-example-data
has
stagnated
and
is
not
par
with
performance
"hybrid"
algorithms
combining
expert
knowledge.
This
Perspective
examines
possible
causes
these
shortcomings,
extending
beyond
established
reasoning
insufficient
quantities
reaction
data.
Drawing
attention
to
intricacies
data
biases
that
are
specific
domain
synthetic
we
advocate
augmenting
unique
capabilities
knowledge
base
strategies
experts.
By
actively
involving
chemists,
who
end
users
any
software,
into
process,
envision
bridge
gap
between
computer
intricate
nature
synthesis.
Advanced Science,
Journal Year:
2024,
Volume and Issue:
11(26)
Published: May 5, 2024
Abstract
Self‐assembling
peptides
have
numerous
applications
in
medicine,
food
chemistry,
and
nanotechnology.
However,
their
discovery
has
traditionally
been
serendipitous
rather
than
driven
by
rational
design.
Here,
HydrogelFinder,
a
foundation
model
is
developed
for
the
design
of
self‐assembling
from
scratch.
This
explores
self‐assembly
properties
molecular
structure,
leveraging
1,377
non‐peptidal
small
molecules
to
navigate
chemical
space
improve
structural
diversity.
Utilizing
111
peptide
candidates
are
generated
synthesized
17
peptides,
subsequently
experimentally
validating
biophysical
characteristics
nine
ranging
1–10
amino
acids—all
achieved
within
19‐day
workflow.
Notably,
two
de
novo‐designed
demonstrated
low
cytotoxicity
biocompatibility,
as
confirmed
live/dead
assays.
work
highlights
capacity
HydrogelFinder
diversify
through
molecules,
offering
powerful
toolkit
paradigm
future
endeavors.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(21), P. 14566 - 14575
Published: April 24, 2024
Due
to
the
increased
concern
about
energy
and
environmental
issues,
significant
attention
has
been
paid
development
of
large-scale
storage
devices
facilitate
utilization
clean
sources.
The
redox
flow
battery
(RFB)
is
one
most
promising
systems.
Recently,
high
cost
transition-metal
complex-based
RFB
promoted
aqueous
RFBs
with
redox-active
organic
molecules.
To
expand
working
voltage,
computational
chemistry
applied
search
for
molecules
lower
or
higher
potentials.
However,
potential
computation
based
on
implicit
solvation
models
would
be
challenging
due
difficulty
in
parametrization
when
considering
complex
supporting
electrolytes.
Besides,
although
ab
initio
molecular
dynamics
(AIMD)
describes
electrolytes
same
level
electronic
structure
theory
as
couple,
application
impeded
by
costs.
machine
learning
(MLMD)
illustrated
accelerate
AIMD
several
orders
magnitude
without
sacrificing
accuracy.
It
established
that
potentials
can
computed
MLMD
two
separated
(MLPs)
reactant
product
states,
which
redundant
inefficient.
In
this
work,
an
automated
workflow
developed
construct
a
universal
MLP
both
compute
acidity
constants
more
efficiently.
Furthermore,
predicted
evaluated
at
hybrid
functional
much
costs,
design
RFBs.