Langmuir,
Год журнала:
2024,
Номер
41(1), С. 858 - 867
Опубликована: Дек. 30, 2024
The
first
protocells
are
speculated
to
have
arisen
from
the
self-assembly
of
simple
abiotic
carboxylic
acids,
alcohols,
and
other
amphiphiles
into
vesicles.
To
study
complex
process
vesicle
formation,
we
combined
laboratory
automation
with
AI-guided
experimentation
accelerate
discovery
specific
compositions
underlying
principles
governing
formation.
Using
a
low-cost
commercial
liquid
handling
robot,
automated
experimental
procedures,
enabling
high-throughput
testing
various
reaction
conditions
for
mixtures
seven
(7)
amphiphiles.
Multitemplate
multiscale
template
matching
(MMTM)
was
used
automate
confocal
microscopy
image
analysis,
us
quantify
formation
without
tedious
manual
counting.
results
were
create
Gaussian
surrogate
model,
then
active
learning
iteratively
direct
experiments
reduce
model
uncertainty.
Mixtures
containing
primarily
trimethyl
decylammonium
decylsulfate
in
equal
amounts
formed
vesicles
at
submillimolar
critical
concentrations,
more
than
20%
glycerol
monodecanoate
prevented
forming
even
high
total
amphiphile
concentrations.
Chemical Reviews,
Год журнала:
2024,
Номер
124(16), С. 9633 - 9732
Опубликована: Авг. 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,
Год журнала:
2025,
Номер
64(9), С. 4637 - 4668
Опубликована: Фев. 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,
Год журнала:
2024,
Номер
3(7), С. 1273 - 1279
Опубликована: Янв. 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,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 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,
Год журнала:
2025,
Номер
2(4), С. 210 - 228
Опубликована: Март 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
Systems,
Год журнала:
2025,
Номер
13(4), С. 253 - 253
Опубликована: Апрель 3, 2025
A
self-driving
lab
(SDL)
system
that
automates
experimental
design,
data
collection,
and
analysis
using
robotics
artificial
intelligence
(AI)
technologies.
Its
significance
has
grown
substantially
in
recent
years.
This
study
analyzes
the
overall
SDL
research
trends,
examines
changes
specific
topics,
visualizes
relational
structure
between
authors
to
identify
key
contributors,
extracts
major
themes
from
extensive
texts
highlight
essential
content.
To
achieve
these
objectives,
trend
analysis,
network
topic
modeling
were
conducted
on
352
papers
collected
Web
of
Science
2004
2023.
ensure
validity
results,
a
correlation
matrix
was
also
performed.
The
results
revealed
three
findings.
First,
surged
since
2019,
driven
by
advancements
AI
technologies,
reflecting
heightened
activity
this
field.
Second,
modern
scientific
is
advancing
with
focus
data-driven
approaches,
applications,
optimization
through
utilization
SDLs.
Third,
exhibits
interdisciplinary
convergence,
encompassing
material
optimization,
biological
processes,
predictive
algorithms.
underscores
growing
importance
SDLs
as
tool
across
diverse
academic
disciplines
provides
practical
insights
into
sustainable
future
directions
strategic
approaches.
