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.
Artificial Intelligence Chemistry,
Год журнала:
2024,
Номер
2(1), С. 100049 - 100049
Опубликована: Янв. 19, 2024
Artificial
intelligence
(AI)
is
driving
a
revolution
in
chemistry,
reshaping
the
landscape
of
molecular
design.
This
review
explores
AI's
pivotal
roles
field
organic
synthesis
applications.
AI
accurately
predicts
reaction
outcomes,
controls
chemical
selectivity,
simplifies
planning,
accelerates
catalyst
discovery,
and
fuels
material
innovation
so
on.
It
seamlessly
integrates
data-driven
algorithms
with
intuition
to
redefine
As
chemistry
advances,
it
promises
accelerated
research,
sustainability,
innovative
solutions
chemistry's
pressing
challenges.
The
fusion
poised
shape
field's
future
profoundly,
offering
new
horizons
precision
efficiency.
encapsulates
transformation
marking
moment
where
data
converge
revolutionize
world
molecules.
ACS Nano,
Год журнала:
2024,
Номер
18(35), С. 23842 - 23875
Опубликована: Авг. 22, 2024
Machine
learning
(ML)
using
data
sets
of
atomic
and
molecular
force
fields
(FFs)
has
made
significant
progress
provided
benefits
in
the
chemistry
material
science.
This
work
examines
interactions
between
materials
computational
science
at
scales
for
metal-organic
framework
(MOF)
adsorbent
development
toward
carbon
dioxide
(CO
Chemical Science,
Год журнала:
2024,
Номер
15(31), С. 12200 - 12233
Опубликована: Янв. 1, 2024
AI
and
automation
are
revolutionizing
catalyst
discovery,
shifting
from
manual
methods
to
high-throughput
digital
approaches,
enhanced
by
large
language
models.
Abstract
Material
science
has
historically
evolved
in
tandem
with
advancements
technologies
for
characterization,
synthesis,
and
computation.
Another
type
of
technology
to
add
this
mix
is
machine
learning
(ML)
artificial
intelligence
(AI).
Now
increasingly
sophisticated
AI‐models
are
seen
that
can
solve
progressively
harder
problems
across
a
variety
fields.
From
material
perspective,
it
indisputable
offer
potent
toolkit
the
potential
substantially
accelerate
research
efforts
areas
such
as
development
discovery
new
functional
materials.
Less
clear
how
best
harness
development,
what
skill
sets
will
be
required,
may
affect
established
practices.
In
paper,
those
question
explored
respect
more
ML/AI‐approaches.
To
structure
discussion,
conceptual
framework
an
AI‐ladder
introduced.
This
ranges
from
basic
data‐fitting
techniques
advanced
functionalities
semi‐autonomous
experimentation,
experimental
design,
knowledge
generation,
hypothesis
formulation,
orchestration
specialized
AI
modules
stepping‐stones
toward
general
intelligence.
ladder
metaphor
provides
hierarchical
contemplating
opportunities,
challenges,
evolving
required
stay
competitive
age
ACS Materials Letters,
Год журнала:
2024,
Номер
6(4), С. 1347 - 1355
Опубликована: Март 8, 2024
For
solar-driven
overall
pure
water
splitting,
a
superior
photocatalyst
with
reasonable
atomic
and
electronic
structure
is
needed
to
be
suitable
for
both
half-reactions,
HER
OER.
TiO2
has
showcased
remarkable
catalytic
efficiency
in
the
field
of
but
it
still
encounters
obstacles
accomplishing
proficient
splitting.
Within
this
work,
following
sequential
screening
based
on
element
type,
stability,
structure,
adsorption
energy,
we
designed
TiO2-based
catalyst
workflow
This
DFT-based
significantly
reduced
time
trial-and-error
costs
associated
traditional
experimental
design.
It
precisely
guided
synthesis
highly
dispersed
Cu-loaded/N-doped
TiO2,
which
facilitated
sacrificial-agent-free
resulting
solar
fuel
0.2%
an
H2
yield
1027.7
μmol/h/g.
Advanced
DFT
calculations
revealed
that
d–p
orbital
coupling
between
Cu
N
broke
scaling
relationship
O-based
intermediates.
work
holds
promise
extension
other
reactions,
offering
valuable
insights
into
design
endeavors.
Journal of the American Chemical Society,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 8, 2025
The
successful
integration
of
large
language
models
(LLMs)
into
laboratory
workflows
has
demonstrated
robust
capabilities
in
natural
processing,
autonomous
task
execution,
and
collaborative
problem-solving.
This
offers
an
exciting
opportunity
to
realize
the
dream
chemical
research
on
demand.
Here,
we
report
a
robotic
AI
chemist
powered
by
hierarchical
multiagent
system,
ChemAgents,
based
on-board
Llama-3.1-70B
LLM,
capable
executing
complex,
multistep
experiments
with
minimal
human
intervention.
It
operates
through
Task
Manager
agent
that
interacts
researchers
coordinates
four
role-specific
agents─Literature
Reader,
Experiment
Designer,
Computation
Performer,
Robot
Operator─each
leveraging
one
foundational
resources:
comprehensive
Literature
Database,
extensive
Protocol
Library,
versatile
Model
state-of-the-art
Automated
Lab.
We
demonstrate
its
versatility
efficacy
six
experimental
tasks
varying
complexity,
ranging
from
straightforward
synthesis
characterization
more
complex
exploration
screening
parameters,
culminating
discovery
optimization
functional
materials.
Additionally,
introduce
seventh
task,
where
ChemAgents
is
deployed
new
chemistry
lab
environment
autonomously
perform
photocatalytic
organic
reactions,
highlighting
ChemAgents's
scalability
adaptability.
Our
multiagent-driven
showcases
potential
on-demand
accelerate
democratize
access
advanced
across
academic
disciplines
industries.
Nature Communications,
Год журнала:
2024,
Номер
15(1)
Опубликована: Ноя. 23, 2024
The
rapid
emergence
of
large
language
model
(LLM)
technology
presents
promising
opportunities
to
facilitate
the
development
synthetic
reactions.
In
this
work,
we
leveraged
power
GPT-4
build
an
LLM-based
reaction
framework
(LLM-RDF)
handle
fundamental
tasks
involved
throughout
chemical
synthesis
development.
LLM-RDF
comprises
six
specialized
agents,
including
Literature
Scouter,
Experiment
Designer,
Hardware
Executor,
Spectrum
Analyzer,
Separation
Instructor,
and
Result
Interpreter,
which
are
pre-prompted
accomplish
designated
tasks.
A
web
application
with
as
backend
was
built
allow
chemist
users
interact
automated
experimental
platforms
analyze
results
via
natural
language,
thus,
eliminating
need
for
coding
skills
ensuring
accessibility
all
chemists.
We
demonstrated
capabilities
in
guiding
end-to-end
process
copper/TEMPO
catalyzed
aerobic
alcohol
oxidation
aldehyde
reaction,
literature
search
information
extraction,
substrate
scope
condition
screening,
kinetics
study,
optimization,
scale-up
product
purification.
Furthermore,
LLM-RDF's
broader
applicability
versability
validated
on
various
three
distinct
reactions
(SNAr
photoredox
C-C
cross-coupling
heterogeneous
photoelectrochemical
reaction).
rise
offers
new
advancing
synthesis.
Here,
authors
developed
copilot
design
