ACS Materials Letters,
Journal Year:
2024,
Volume and Issue:
6(11), P. 5103 - 5111
Published: Oct. 15, 2024
With
the
growing
interest
of
electrochemical
community
in
high-throughput
(HT)
experimentation
as
a
powerful
tool
accelerating
materials
discovery,
implementation
HT
methodologies
and
design
workflows
has
gained
traction.
We
identify
6
aspects
essential
to
workflow
electrochemistry
beyond
ease
incorporation
methods
community's
research
assist
their
improvement.
study
IrCo
mixed-metal
oxides
(MMOs)
for
oxygen
evolution
reaction
(OER)
acidic
media
using
mentioned
provide
practical
example
possible
pitfalls
strategies
counteract
them.
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.
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.
Digital Discovery,
Journal Year:
2024,
Volume and Issue:
3(7), P. 1273 - 1279
Published: Jan. 1, 2024
We
share
the
results
of
a
survey
on
automation
and
autonomy
in
materials
science
labs,
which
highlight
variety
researcher
challenges
motivations.
also
propose
framework
for
levels
laboratory
from
L0
to
L5.
Digital Discovery,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
Archerfish
is
a
low-cost,
high-throughput
tool
for
combinatorial
materials
research.
Retrofitted
with
in
situ
mixing,
prints
250
unique
compositions
per
min—a
100×
acceleration
factor—for
aqueous,
nanoparticle,
and
crystalline
materials.
Chem & Bio Engineering,
Journal Year:
2025,
Volume and Issue:
2(4), P. 210 - 228
Published: March 5, 2025
As
the
chemical
industry
shifts
toward
sustainable
practices,
there
is
a
growing
initiative
to
replace
conventional
fossil-derived
solvents
with
environmentally
friendly
alternatives
such
as
ionic
liquids
(ILs)
and
deep
eutectic
(DESs).
Artificial
intelligence
(AI)
plays
key
role
in
discovery
design
of
novel
development
green
processes.
This
review
explores
latest
advancements
AI-assisted
solvent
screening
specific
focus
on
machine
learning
(ML)
models
for
physicochemical
property
prediction
separation
process
design.
Additionally,
this
paper
highlights
recent
progress
automated
high-throughput
(HT)
platforms
screening.
Finally,
discusses
challenges
prospects
ML-driven
HT
strategies
optimization.
To
end,
provides
insights
advance
future
APL Machine Learning,
Journal Year:
2025,
Volume and Issue:
3(2)
Published: April 30, 2025
Machine
learning
and
automation
are
transforming
scientific
research,
yet
the
implementation
of
self-driving
laboratories
(SDLs)
remains
costly
complex,
it
difficult
to
learn
how
use
these
facilities.
To
address
this,
we
introduce
Claude-Light,
a
lightweight,
remotely
accessible
instrument
designed
for
prototyping
algorithms
machine
workflows.
Claude-Light
integrates
REST
API,
Raspberry
Pi-based
control
system,
an
RGB
LED
with
photometer
that
measures
ten
spectral
outputs,
providing
controlled
but
realistic
experimental
environment.
This
device
enables
users
explore
at
multiple
levels,
from
basic
programming
design
learning-driven
optimization.
We
demonstrate
application
in
structured
approaches,
including
traditional
scripting,
statistical
experiments,
active
methods.
In
addition,
role
large
language
models
(LLMs)
laboratory
automation,
highlighting
their
selection,
data
extraction,
function
calling,
code
generation.
While
LLMs
present
new
opportunities
streamlining
they
also
challenges
related
reproducibility,
security,
reliability.
discuss
strategies
mitigate
risks
while
leveraging
enhanced
efficiency
laboratories.
provides
practical
platform
students
researchers
develop
skills
test
before
deploying
them
larger-scale
SDLs.
By
lowering
barrier
entry
this
tool
facilitates
broader
adoption
AI-driven
experimentation
fosters
innovation
autonomous
