Science and Technology of Advanced Materials Methods,
Journal Year:
2023,
Volume and Issue:
3(1)
Published: April 3, 2023
The
emergence
of
autonomous
experimental
systems
integrating
machine
learning
and
robots
is
ushering
in
a
paradigm
shift
materials
science.
Using
computer
algorithms
to
decide
perform
all
steps,
these
require
no
human
intervention.
A
current
direction
focuses
on
discovering
unexpected
theories
with
unconventional
research
approaches.
This
article
reviews
the
latest
achievements
discusses
impact
systems,
which
will
fundamentally
change
way
we
understand
research.
Moreover,
as
continue
develop,
need
think
about
role
researchers
becomes
more
pressing.
While
robotics
can
free
us
from
repetitive
aspects
research,
strengths
limitations
focus
how
humans
higher
creativity.
In
addition,
also
discuss
inventorship
authorship
era
systems.
Accounts of Chemical Research,
Journal Year:
2022,
Volume and Issue:
55(17), P. 2454 - 2466
Published: Aug. 10, 2022
We
must
accelerate
the
pace
at
which
we
make
technological
advancements
to
address
climate
change
and
disease
risks
worldwide.
This
swifter
of
discovery
requires
faster
research
development
cycles
enabled
by
better
integration
between
hypothesis
generation,
design,
experimentation,
data
analysis.
Typical
take
months
years.
However,
data-driven
automated
laboratories,
or
self-driving
can
significantly
molecular
materials
discovery.
Recently,
substantial
have
been
made
in
areas
machine
learning
optimization
algorithms
that
allowed
researchers
extract
valuable
knowledge
from
multidimensional
sets.
Machine
models
be
trained
on
large
sets
literature
databases,
but
their
performance
often
hampered
a
lack
negative
results
metadata.
In
contrast,
generated
laboratories
information-rich,
containing
precise
details
experimental
conditions
Consequently,
much
larger
amounts
high-quality
are
gathered
laboratories.
When
placed
open
repositories,
this
used
community
reproduce
experiments,
for
more
in-depth
analysis,
as
basis
further
investigation.
Accordingly,
will
increase
accessibility
reproducibility
science,
is
sorely
needed.In
Account,
describe
our
efforts
build
lab
new
class
materials:
organic
semiconductor
lasers
(OSLs).
Since
they
only
recently
demonstrated,
little
known
about
material
design
rules
thin-film,
electrically-pumped
OSL
devices
compared
other
technologies
such
light-emitting
diodes
photovoltaics.
To
realize
high-performing
materials,
developing
flexible
system
synthesis
via
iterative
Suzuki-Miyaura
cross-coupling
reactions.
platform
directly
coupled
analysis
purification
capabilities.
Subsequently,
molecules
interest
transferred
an
optical
characterization
setup.
currently
limited
measurements
solution.
properties
ultimately
most
important
solid
state
(e.g.,
thin-film
device).
end
different
scientific
goal,
inorganic
focused
oxygen
evolution
reaction.While
future
very
promising,
numerous
challenges
still
need
overcome.
These
split
into
cognition
motor
function.
Generally,
cognitive
related
with
constraints
unexpected
outcomes
general
algorithmic
solutions
yet
developed.
A
practical
challenge
could
resolved
near
software
control
because
few
instrument
manufacturers
products
mind.
Challenges
function
largely
handling
heterogeneous
systems,
dispensing
solids
performing
extractions.
As
result,
it
critical
understand
adapting
procedures
were
designed
human
experimenters
not
simple
transferring
those
same
actions
system,
there
may
efficient
ways
achieve
goal
fashion.
carefully
rethink
translation
manual
protocols.
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.
Science,
Journal Year:
2024,
Volume and Issue:
386(6727), P. 1256 - 1264
Published: Dec. 12, 2024
The
inverse
design
of
tailored
organic
molecules
for
specific
optoelectronic
devices
high
complexity
holds
an
enormous
potential
but
has
not
yet
been
realized.
Current
models
rely
on
large
data
sets
that
generally
do
exist
specialized
research
fields.
We
demonstrate
a
closed-loop
workflow
combines
high-throughput
synthesis
semiconductors
to
create
datasets
and
Bayesian
optimization
discover
new
hole-transporting
materials
with
properties
solar
cell
applications.
predictive
were
based
molecular
descriptors
allowed
us
link
the
structure
these
their
performance.
A
series
high-performance
identified
from
minimal
suggestions
achieved
up
26.2%
(certified
25.9%)
power
conversion
efficiency
in
perovskite
cells.
Self-driving
laboratories
(SDLs),
which
combine
automated
experimental
hardware
with
computational
experiment
planning,
have
emerged
as
powerful
tools
for
accelerating
materials
discovery.
The
intrinsic
complexity
created
by
their
multitude
of
components
requires
an
effective
orchestration
platform
to
ensure
the
correct
operation
diverse
setups.
Existing
frameworks,
however,
are
either
tailored
specific
setups
or
not
been
implemented
real-world
synthesis.
To
address
these
issues,
we
introduce
ChemOS
2.0,
architecture
that
efficiently
coordinates
communication,
data
exchange,
and
instruction
management
among
modular
laboratory
components.
By
treating
"operating
system"
2.0
combines
ab-initio
calculations,
statistical
algorithms
guide
closed-loop
operations.
demonstrate
its
capabilities,
showcase
in
a
case
study
focused
on
discovering
organic
laser
molecules.
results
confirm
2.0's
prowess
research
potential
valuable
design
future
SDL
platforms.
RSC Sustainability,
Journal Year:
2024,
Volume and Issue:
2(5), P. 1300 - 1336
Published: Jan. 1, 2024
Scientists
are
of
key
importance
to
the
society
advocate
awareness
climate
crisis
and
its
underlying
scientific
evidence
provide
solutions
for
a
sustainable
future.
As
much
as
research
has
led
great
achievements
benefits,
traditional
laboratory
practices
come
with
unintended
environmental
consequences.
Scientists,
while
providing
problems
educating
young
innovators
future,
also
part
problem:
excessive
energy
consumption,
(hazardous)
waste
generation,
resource
depletion.
Through
their
own
operations,
science,
laboratories
have
significant
carbon
footprint
contribute
crisis.
Climate
change
requires
rapid
response
across
all
sectors
society,
modeled
by
inspiring
leaders.
A
broader
community
that
takes
concrete
actions
would
serve
an
important
step
in
convincing
general
public
similar
actions.
Over
past
years,
grassroots
movements
sciences
recognized
overlooked
impact
enterprise,
so-called
Green
Lab
initiatives
emerged
seeking
address
research.
Driven
voluntary
efforts
researchers
staff,
they
educate
peers,
develop
sustainability
guidelines,
write
publications
maintain
accreditation
frameworks.
With
this
perspective
we
want
spark
leadership
promote
systemic
approach
Comprehensive
root-causes
is
presented,
expanded
data
from
current
case
study
University
Groningen
showcasing
annual
savings
398
763
€
well
477.1
tons
CO
Energy & Environmental Science,
Journal Year:
2024,
Volume and Issue:
17(15), P. 5490 - 5499
Published: Jan. 1, 2024
Using
a
fully
automated
device
acceleration
platform
(DAP)
to
systematically
optimize
air-processed
parameters
and
establish
standard
operation
procedure
(SOP)
for
preparing
high-performance
perovskite
solar
cells
under
ambient
air.
Advanced Energy Materials,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 2, 2025
Abstract
Traditional
optimization
methods
often
face
challenges
in
exploring
complex
process
parameter
spaces,
which
typically
result
suboptimal
local
maxima.
Here
an
autonomous
framework
driven
by
a
machine
learning
(ML)‐guided
automated
platform
is
introduced
to
optimize
the
fabrication
conditions
of
additive‐
and
passivation‐free
perovskite
solar
cells
(PSCs)
under
ambient
conditions.
By
effectively
6D
space,
this
method
identifies
five
sets
achieving
efficiencies
above
23%,
with
peak
efficiency
23.7%
limited
experimental
budgets.
Feature
importance
analysis
indicates
that
rotation
speeds
during
first
second
steps
processing
are
most
influential
factors
affecting
device
performance,
thereby
meriting
prioritization
efforts.
These
results
demonstrate
exceptional
capability
addressing
its
potential
advance
photovoltaic
technology.
Beyond
PSCs,
work
provides
reliable
comprehensive
strategy
for
optimizing
solution‐processed
semiconductors
highlights
broader
applications
methodologies
materials
science.