Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis
Nature Catalysis,
Год журнала:
2025,
Номер
8(1), С. 13 - 19
Опубликована: Янв. 29, 2025
Язык: Английский
Bayesian Optimization as a Sustainable Strategy for Early-Stage Process Development? A Case Study of Cu-Catalyzed C–N Coupling of Sterically Hindered Pyrazines
ACS Sustainable Chemistry & Engineering,
Год журнала:
2023,
Номер
11(28), С. 10545 - 10554
Опубликована: Июль 7, 2023
Bayesian
optimization
is
a
powerful
machine
learning
technique
that
particularly
well-suited
for
optimizing
chemical
reactions
in
the
early
stages
of
process
development.
It
can
efficiently
explore
vast
reaction
spaces
and
predict
high-yielding
conditions
by
evaluating
only
small
number
experiments.
In
this
report,
we
investigated
potential
as
tool
to
enhance
sustainability
synthesis.
Specifically,
focused
on
real-world
early-stage
development
example:
C–N
coupling
sterically
encumbered
bromo-pyrazines
with
amines.
Our
objective
was
identify
sustainable
utilize
Earth-abundant
copper
catalysts
non-hazardous
solvents.
We
used
optimizers
various
acquisition
functions.
assessed
their
performance
identified
key
features
affecting
results.
The
optimized
enabled
synthesis
range
pyrazines
pyridines
moderate
excellent
yields.
Язык: Английский
Accelerated exploration of heterogeneous CO2 hydrogenation catalysts by Bayesian-optimized high-throughput and automated experimentation
Chem Catalysis,
Год журнала:
2024,
Номер
4(2), С. 100888 - 100888
Опубликована: Янв. 21, 2024
Язык: Английский
Combining Bayesian optimization and automation to simultaneously optimize reaction conditions and routes
Chemical Science,
Год журнала:
2024,
Номер
15(20), С. 7732 - 7741
Опубликована: Янв. 1, 2024
Combining
a
cloud-based
Bayesian
optimization
platform
with
robotic
synthesis
accelerated
the
discovery
of
high
conversion
iodination
terminal
alkyne
reactions
in
large
search
space
over
12
000
possible
23
experiments.
Язык: Английский
Research Trend Analysis in the Field of Self-Driving Labs Using Network Analysis and Topic Modeling
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.
Язык: Английский
BayBE: a Bayesian Back End for experimental planning in the low-to-no-data regime
Digital Discovery,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
The
Bayesian
Back
End
(BayBE)
has
a
range
of
advanced
features
enabling
scientists
to
go
beyond
the
basic
optimization
loop
and
readily
tackle
real
world
experimental
campaigns.
Язык: Английский
The Lab of the Future: Self-Driving Labs for Molecule Discovery
GEN Biotechnology,
Год журнала:
2024,
Номер
3(2), С. 83 - 86
Опубликована: Апрель 1, 2024
Self-driving
laboratories
(SDLs)
are
at
the
intersection
of
robotics,
artificial
intelligence,
and
laboratory
automation.
From
materials
design,
small
molecule
discovery,
to
synthetic
biology,
SDLs
have
infiltrated
a
diverse
range
applications
across
academia
industry.
The
following
Perspective
(inspired
by
Future
Labs,
workshop
held
NC
State
University
in
early
2024)
describes
how
might
become
an
integrated
component
future
research
processes
generally
applicable
many
development
areas
for
increased
innovation
discovery.
Язык: Английский
CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space
Journal of Cheminformatics,
Год журнала:
2024,
Номер
16(1)
Опубликована: Май 10, 2024
Abstract
Chemical
reaction
optimization
(RO)
is
an
iterative
process
that
results
in
large,
high-dimensional
datasets.
Current
tools
allow
for
only
limited
analysis
and
understanding
of
parameter
spaces,
making
it
hard
scientists
to
review
or
follow
changes
throughout
the
process.
With
recent
emergence
using
artificial
intelligence
(AI)
models
aid
RO,
another
level
complexity
has
been
added.
Helping
assess
quality
a
model’s
prediction
understand
its
decision
critical
supporting
human-AI
collaboration
trust
calibration.
To
address
this,
we
propose
CIME4R—an
open-source
interactive
web
application
analyzing
RO
data
AI
predictions.
CIME4R
supports
users
(
i
)
comprehending
space,
ii
investigating
how
developed
over
iterations,
iii
identifying
factors
reaction,
iv
model
This
facilitates
informed
decisions
during
helps
completed
process,
especially
AI-guided
RO.
aids
decision-making
through
interaction
between
humans
by
combining
strengths
expert
experience
high
computational
precision.
We
tested
with
domain
experts
verified
usefulness
three
case
studies.
Using
were
able
produce
valuable
insights
from
past
campaigns
make
on
which
experiments
perform
next.
believe
beginning
community
project
potential
improve
workflow
working
domain.
Scientific
contribution
best
our
knowledge,
first
tailored
peculiar
requirements
campaigns.
Due
growing
use
special
focus
facilitating
models.
evaluated
verify
practical
usefulness.
Язык: Английский
Highlights from the 56th Bürgenstock Conference on Stereochemistry 2023
Chemical Science,
Год журнала:
2023,
Номер
14(35), С. 9244 - 9247
Опубликована: Янв. 1, 2023
Herein,
we
share
an
overview
of
the
scientific
highlights
from
speakers
at
latest
edition
longstanding
Bürgenstock
Conference.
Язык: Английский
CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space
Опубликована: Дек. 22, 2023
Chemical
reaction
optimization
(RO)
is
an
iterative
process
that
results
in
large
and
high-dimensional
datasets.
Current
tools
only
allow
for
limited
analysis
understanding
of
parameter
spaces,
making
it
hard
scientists
to
review
or
follow
changes
throughout
the
process.
With
recent
emergence
using
artificial
intelligence
(AI)
models
aid
RO,
another
level
complexity
was
added.
It
critical
assess
quality
a
model’s
prediction
understand
its
decision
human-AI
collaboration
trust
calibration.
To
regard,
we
propose
CIME4R—an
open-source
interactive
web
application
analyzing
RO
data
AI
predictions.
CIME4R
supports
users
(i)
comprehending
space,
(ii)
investigating
how
developed
over
iterations,
(iii)
identifying
factors
reaction,
(iv)
model
This
aids
informed
decisions
during
helps
them
retrospect,
especially
realm
AI-guided
RO.
decision-making
through
interaction
between
humans
by
combining
strengths
expert
experience
high
computational
precision.
We
tested
together
with
domain
experts
verified
usefulness
three
case
studies.
were
able
produce
valuable
insights
from
past
campaigns
make
on
which
experiments
perform
next.
believe
beginning
community
project
improves
workflow
working
domain.
Язык: Английский