Journal of Chemical Information and Modeling,
Journal Year:
2023,
Volume and Issue:
63(12), P. 3659 - 3668
Published: June 14, 2023
Machine
learning
models
are
increasingly
being
utilized
to
predict
outcomes
of
organic
chemical
reactions.
A
large
amount
reaction
data
is
used
train
these
models,
which
in
stark
contrast
how
expert
chemists
discover
and
develop
new
reactions
by
leveraging
information
from
a
small
number
relevant
transformations.
Transfer
active
two
strategies
that
can
operate
low-data
situations,
may
help
fill
this
gap
promote
the
use
machine
for
tackling
real-world
challenges
synthesis.
This
Perspective
introduces
transfer
connects
potential
opportunities
directions
further
research,
especially
area
prospective
development
Science,
Journal Year:
2024,
Volume and Issue:
383(6681)
Published: Jan. 25, 2024
The
optimization,
intensification,
and
scale-up
of
photochemical
processes
constitute
a
particular
challenge
in
manufacturing
environment
geared
primarily
toward
thermal
chemistry.
In
this
work,
we
present
versatile
flow-based
robotic
platform
to
address
these
challenges
through
the
integration
readily
available
hardware
custom
software.
Our
open-source
combines
liquid
handler,
syringe
pumps,
tunable
continuous-flow
photoreactor,
inexpensive
Internet
Things
devices,
an
in-line
benchtop
nuclear
magnetic
resonance
spectrometer
enable
automated,
data-rich
optimization
with
closed-loop
Bayesian
strategy.
A
user-friendly
graphical
interface
allows
chemists
without
programming
or
machine
learning
expertise
easily
monitor,
analyze,
improve
photocatalytic
reactions
respect
both
continuous
discrete
variables.
system's
effectiveness
was
demonstrated
by
increasing
overall
reaction
yields
improving
space-time
compared
those
previously
reported
processes.
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.
Chemical Science,
Journal Year:
2023,
Volume and Issue:
14(20), P. 5243 - 5265
Published: Jan. 1, 2023
This
review
provides
a
multidisciplinary
overview
of
the
challenges
and
opportunities
for
dynamic
covalent
chemistry-based
macromolecules
towards
design
new,
sustainable,
recyclable
materials
circular
economy.
Digital Discovery,
Journal Year:
2023,
Volume and Issue:
3(1), P. 23 - 33
Published: Dec. 6, 2023
The
ASLLA
Symposium
focused
on
accelerating
chemical
science
with
AI.
Discussions
data,
new
applications,
algorithms,
and
education
were
summarized.
Recommendations
for
researchers,
educators,
academic
bodies
provided.
Journal of the American Chemical Society,
Journal Year:
2023,
Volume and Issue:
145(40), P. 21699 - 21716
Published: Sept. 27, 2023
Exceptional
molecules
and
materials
with
one
or
more
extraordinary
properties
are
both
technologically
valuable
fundamentally
interesting,
because
they
often
involve
new
physical
phenomena
compositions
that
defy
expectations.
Historically,
exceptionality
has
been
achieved
through
serendipity,
but
recently,
machine
learning
(ML)
automated
experimentation
have
widely
proposed
to
accelerate
target
identification
synthesis
planning.
In
this
Perspective,
we
argue
the
data-driven
methods
commonly
used
today
well-suited
for
optimization
not
realization
of
exceptional
molecules.
Finding
such
outliers
should
be
possible
using
ML,
only
by
shifting
away
from
traditional
ML
approaches
tweak
composition,
crystal
structure,
reaction
pathway.
We
highlight
case
studies
high-Tc
oxide
superconductors
superhard
demonstrate
challenges
ML-guided
discovery
discuss
limitations
automation
task.
then
provide
six
recommendations
development
capable
discovery:
(i)
Avoid
tyranny
middle
focus
on
extrema;
(ii)
When
data
limited,
qualitative
predictions
direction
than
interpolative
accuracy;
(iii)
Sample
what
can
made
how
make
it
defer
optimization;
(iv)
Create
room
(and
look)
unexpected
while
pursuing
your
goal;
(v)
Try
fill-in-the-blanks
input
output
space;
(vi)
Do
confuse
human
understanding
model
interpretability.
conclude
a
description
these
integrated
into
workflows,
which
enable
materials.
Nature Communications,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: Jan. 23, 2024
Abstract
The
ability
to
integrate
resources
and
share
knowledge
across
organisations
empowers
scientists
expedite
the
scientific
discovery
process.
This
is
especially
crucial
in
addressing
emerging
global
challenges
that
require
solutions.
In
this
work,
we
develop
an
architecture
for
distributed
self-driving
laboratories
within
World
Avatar
project,
which
seeks
create
all-encompassing
digital
twin
based
on
a
dynamic
graph.
We
employ
ontologies
capture
data
material
flows
design-make-test-analyse
cycles,
utilising
autonomous
agents
as
executable
components
carry
out
experimentation
workflow.
Data
provenance
recorded
ensure
its
findability,
accessibility,
interoperability,
reusability.
demonstrate
practical
application
of
our
framework
by
linking
two
robots
Cambridge
Singapore
collaborative
closed-loop
optimisation
pharmaceutically-relevant
aldol
condensation
reaction
real-time.
graph
autonomously
evolves
toward
scientist’s
research
goals,
with
effectively
generating
Pareto
front
cost-yield
three
days.
Journal of Materials Science,
Journal Year:
2024,
Volume and Issue:
59(31), P. 14095 - 14140
Published: July 30, 2024
Abstract
Electrospun
nanofibers
have
gained
prominence
as
a
versatile
material,
with
applications
spanning
tissue
engineering,
drug
delivery,
energy
storage,
filtration,
sensors,
and
textiles.
Their
unique
properties,
including
high
surface
area,
permeability,
tunable
porosity,
low
basic
weight,
mechanical
flexibility,
alongside
adjustable
fiber
diameter
distribution
modifiable
wettability,
make
them
highly
desirable
across
diverse
fields.
However,
optimizing
the
properties
of
electrospun
to
meet
specific
requirements
has
proven
be
challenging
endeavor.
The
electrospinning
process
is
inherently
complex
influenced
by
numerous
variables,
applied
voltage,
polymer
concentration,
solution
flow
rate,
molecular
weight
polymer,
needle-to-collector
distance.
This
complexity
often
results
in
variations
nanofibers,
making
it
difficult
achieve
desired
characteristics
consistently.
Traditional
trial-and-error
approaches
parameter
optimization
been
time-consuming
costly,
they
lack
precision
necessary
address
these
challenges
effectively.
In
recent
years,
convergence
materials
science
machine
learning
(ML)
offered
transformative
approach
electrospinning.
By
harnessing
power
ML
algorithms,
scientists
researchers
can
navigate
intricate
space
more
efficiently,
bypassing
need
for
extensive
experimentation.
holds
potential
significantly
reduce
time
resources
invested
producing
wide
range
applications.
Herein,
we
provide
an
in-depth
analysis
current
work
that
leverages
obtain
target
nanofibers.
examining
work,
explore
intersection
ML,
shedding
light
on
advancements,
challenges,
future
directions.
comprehensive
not
only
highlights
processes
but
also
provides
valuable
insights
into
evolving
landscape,
paving
way
innovative
precisely
engineered
various
Graphical
abstract
