Chemical Communications,
Год журнала:
2024,
Номер
60(24), С. 3217 - 3225
Опубликована: Янв. 1, 2024
High-throughput
continuous
flow
technology
has
emerged
as
a
revolutionary
approach
in
chemical
synthesis,
offering
accelerated
experimentation
and
improved
efficiency.
Chemical Science,
Год журнала:
2023,
Номер
14(16), С. 4230 - 4247
Опубликована: Янв. 1, 2023
This
review
explores
the
benefits
of
flow
chemistry
and
dispels
notion
that
it
is
a
mysterious
“black
box”,
demonstrating
how
can
push
boundaries
organic
synthesis
through
understanding
its
governing
principles.
Journal of the American Chemical Society,
Год журнала:
2022,
Номер
144(43), С. 19999 - 20007
Опубликована: Окт. 19, 2022
We
report
the
development
of
an
open-source
experimental
design
via
Bayesian
optimization
platform
for
multi-objective
reaction
optimization.
Using
high-throughput
experimentation
(HTE)
and
virtual
screening
data
sets
containing
high-dimensional
continuous
discrete
variables,
we
optimized
performance
by
fine-tuning
algorithm
components
such
as
encodings,
surrogate
model
parameters,
initialization
techniques.
Having
established
framework,
applied
optimizer
to
real-world
test
scenarios
simultaneous
yield
enantioselectivity
in
a
Ni/photoredox-catalyzed
enantioselective
cross-electrophile
coupling
styrene
oxide
with
two
different
aryl
iodide
substrates.
Starting
no
previous
data,
identified
conditions
that
surpassed
previously
human-driven
campaigns
within
15
24
experiments,
each
substrate,
among
1728
possible
configurations
available
To
make
more
accessible
nonexperts,
developed
graphical
user
interface
(GUI)
can
be
accessed
online
through
web-based
application
incorporated
features
condition
modification
on
fly
visualization.
This
web
does
not
require
software
installation,
removing
any
programming
barrier
use
platform,
which
enables
chemists
integrate
routines
into
their
everyday
laboratory
practices.
The
optimization,
intensification,
and
scale-up
of
photochemical
processes
constitute
a
particular
challenge
in
manufacturing
environment
geared
primarily
toward
thermal
chemistry.
In
this
work,
we
present
versatile
flow-based
robotic
platform
to
address
these
challenges
through
the
integration
readily
available
hardware
custom
software.
Our
open-source
combines
liquid
handler,
syringe
pumps,
tunable
continuous-flow
photoreactor,
inexpensive
Internet
Things
devices,
an
in-line
benchtop
nuclear
magnetic
resonance
spectrometer
enable
automated,
data-rich
optimization
with
closed-loop
Bayesian
strategy.
A
user-friendly
graphical
interface
allows
chemists
without
programming
or
machine
learning
expertise
easily
monitor,
analyze,
improve
photocatalytic
reactions
respect
both
continuous
discrete
variables.
system's
effectiveness
was
demonstrated
by
increasing
overall
reaction
yields
improving
space-time
compared
those
previously
reported
processes.
Nature Communications,
Год журнала:
2023,
Номер
14(1)
Опубликована: Март 14, 2023
Closed-loop,
autonomous
experimentation
enables
accelerated
and
material-efficient
exploration
of
large
reaction
spaces
without
the
need
for
user
intervention.
However,
advanced
materials
with
complex,
multi-step
processes
data
sparse
environments
remains
a
challenge.
In
this
work,
we
present
AlphaFlow,
self-driven
fluidic
lab
capable
discovery
complex
chemistries.
AlphaFlow
uses
reinforcement
learning
integrated
modular
microdroplet
reactor
performing
steps
variable
sequence,
phase
separation,
washing,
continuous
in-situ
spectral
monitoring.
To
demonstrate
power
toward
high
dimensionality
chemistries,
use
to
discover
optimize
synthetic
routes
shell-growth
core-shell
semiconductor
nanoparticles,
inspired
by
colloidal
atomic
layer
deposition
(cALD).
Without
prior
knowledge
conventional
cALD
parameters,
successfully
identified
optimized
novel
route,
up
40
that
outperformed
sequences.
Through
capabilities
closed-loop,
learning-guided
systems
in
exploring
solving
challenges
nanoparticle
syntheses,
while
relying
solely
on
in-house
generated
from
miniaturized
microfluidic
platform.
Further
application
chemistries
beyond
can
lead
fundamental
generation
as
well
route
discoveries
optimization.
Journal of the American Chemical Society,
Год журнала:
2022,
Номер
145(1), С. 110 - 121
Опубликована: Дек. 27, 2022
Optimization
of
the
catalyst
structure
to
simultaneously
improve
multiple
reaction
objectives
(e.g.,
yield,
enantioselectivity,
and
regioselectivity)
remains
a
formidable
challenge.
Herein,
we
describe
machine
learning
workflow
for
multi-objective
optimization
catalytic
reactions
that
employ
chiral
bisphosphine
ligands.
This
was
demonstrated
through
two
sequential
required
in
asymmetric
synthesis
an
active
pharmaceutical
ingredient.
To
accomplish
this,
density
functional
theory-derived
database
>550
ligands
constructed,
designer
chemical
space
mapping
technique
established.
The
protocol
used
classification
methods
identify
catalysts,
followed
by
linear
regression
model
selectivity.
led
prediction
validation
significantly
improved
all
outputs,
suggesting
general
strategy
can
be
readily
implemented
optimizations
where
performance
is
controlled
A
closed-loop,
autonomous
molecular
discovery
platform
driven
by
integrated
machine
learning
tools
was
developed
to
accelerate
the
design
of
molecules
with
desired
properties.
We
demonstrated
two
case
studies
on
dye-like
molecules,
targeting
absorption
wavelength,
lipophilicity,
and
photooxidative
stability.
In
first
study,
experimentally
realized
294
unreported
across
three
automatic
iterations
design-make-test-analyze
cycles
while
exploring
structure-function
space
four
rarely
reported
scaffolds.
each
iteration,
property
prediction
models
that
guided
exploration
learned
structure-property
diverse
scaffold
derivatives,
which
were
multistep
syntheses
a
variety
reactions.
The
second
study
exploited
trained
explored
chemical
previously
discover
nine
top-performing
within
lightly
space.
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.
Nature Chemical Engineering,
Год журнала:
2024,
Номер
1(3), С. 240 - 250
Опубликована: Фев. 27, 2024
Ligands
play
a
crucial
role
in
enabling
challenging
chemical
transformations
with
transition
metal-mediated
homogeneous
catalysts.
Despite
their
undisputed
catalysis,
discovery
and
development
of
ligands
have
proven
to
be
resource-intensive
undertaking.
Here,
response,
we
present
self-driving
catalysis
laboratory,
Fast-Cat,
for
autonomous
resource-efficient
parameter
space
navigation
Pareto-front
mapping
high-temperature,
high-pressure,
gas–liquid
reactions.
Fast-Cat
enables
ligand
benchmarking
multi-objective
catalyst
performance
evaluation
minimal
human
intervention.
Specifically,
utilize
perform
rapid
identification
the
hydroformylation
reaction
between
syngas
(CO
H2)
olefin
(1-octene)
presence
rhodium
various
classes
phosphorus-based
ligands.
By
reactor
benchmarking,
demonstrate
Fast-Cat's
knowledge
scalability,
essential
fine/specialty
industries.
We
report
details
modular
flow
chemistry
platform
its
experiment-selection
strategy
generation
optimized
experimental
conditions
in-house
data
required
supplying
machine-learning
approaches
investigations.
A
is
presented
efficient
high-throughput
screening
using
rhodium-catalyzed
as
case
study.
used
Pareto
map
investigate
varying
several
Angewandte Chemie International Edition,
Год журнала:
2022,
Номер
62(3)
Опубликована: Ноя. 8, 2022
The
optimization
of
multistep
chemical
syntheses
is
critical
for
the
rapid
development
new
pharmaceuticals.
However,
concatenating
individually
optimized
reactions
can
lead
to
inefficient
syntheses,
owing
interdependencies
between
steps.
Herein,
we
develop
an
automated
continuous
flow
platform
simultaneous
telescoped
reactions.
Our
approach
applied
a
Heck
cyclization-deprotection
reaction
sequence,
used
in
synthesis
precursor
1-methyltetrahydroisoquinoline
C5
functionalization.
A
simple
method
multipoint
sampling
with
single
online
HPLC
instrument
was
designed,
enabling
accurate
quantification
each
reaction,
and
in-depth
understanding
pathways.
Notably,
integration
Bayesian
techniques
identified
81
%
overall
yield
just
14
h,
revealed
favorable
competing
pathway
formation
desired
product.