Chemical Reviews,
Journal Year:
2021,
Volume and Issue:
122(2), P. 2752 - 2906
Published: Aug. 10, 2021
Photoinduced
chemical
transformations
have
received
in
recent
years
a
tremendous
amount
of
attention,
providing
plethora
opportunities
to
synthetic
organic
chemists.
However,
performing
photochemical
transformation
can
be
quite
challenge
because
various
issues
related
the
delivery
photons.
These
challenges
barred
widespread
adoption
steps
industry.
past
decade,
several
technological
innovations
led
more
reproducible,
selective,
and
scalable
photoinduced
reactions.
Herein,
we
provide
comprehensive
overview
these
exciting
advances,
including
flow
chemistry,
high-throughput
experimentation,
reactor
design
scale-up,
combination
photo-
electro-chemistry.
Chemical Science,
Journal Year:
2023,
Volume and Issue:
14(16), P. 4230 - 4247
Published: Jan. 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.
ACS Central Science,
Journal Year:
2022,
Volume and Issue:
8(6), P. 825 - 836
Published: June 10, 2022
Computer-aided
synthesis
planning
(CASP)
tools
can
propose
retrosynthetic
pathways
and
forward
reaction
conditions
for
the
of
organic
compounds,
but
limited
availability
context-specific
data
currently
necessitates
experimental
development
to
fully
specify
process
details.
We
plan
optimize
a
CASP-proposed
human-refined
multistep
route
toward
an
exemplary
small
molecule,
sonidegib,
on
modular,
robotic
flow
platform
with
integrated
analytical
technology
(PAT)
data-rich
experimentation.
Human
insights
address
catalyst
deactivation
improve
yield
by
strategic
choices
order
addition.
Multi-objective
Bayesian
optimization
identifies
optimal
values
categorical
continuous
variables
in
involving
3
reactions
(including
heterogeneous
hydrogenation)
1
separation.
The
platform's
modularity,
reconfigurability,
flexibility
convergent
are
shown
be
essential
allowing
variation
downstream
residence
time
processes
controlling
addition
minimize
undesired
reactivity.
Overall,
work
demonstrates
how
automation,
machine
learning,
robotics
enhance
manual
experimentation
through
assistance
idea
generation,
design,
execution,
optimization.
Science,
Journal Year:
2024,
Volume and Issue:
383(6681)
Published: Jan. 25, 2024
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.
JACS Au,
Journal Year:
2022,
Volume and Issue:
2(2), P. 292 - 309
Published: Jan. 10, 2022
High-fidelity
computer-aided
experimentation
is
becoming
more
accessible
with
the
development
of
computing
power
and
artificial
intelligence
tools.
The
advancement
experimental
hardware
also
empowers
researchers
to
reach
a
level
accuracy
that
was
not
possible
in
past.
Marching
toward
next
generation
self-driving
laboratories,
orchestration
both
resources
lies
at
focal
point
autonomous
discovery
chemical
science.
To
achieve
such
goal,
algorithmically
data
representations
standardized
communication
protocols
are
indispensable.
In
this
perspective,
we
recategorize
recently
introduced
approach
based
on
Materials
Acceleration
Platforms
into
five
functional
components
discuss
recent
case
studies
focus
representation
exchange
scheme
between
different
components.
Emerging
technologies
for
interoperable
multi-agent
systems
discussed
their
applications
automation.
We
hypothesize
knowledge
graph
technology,
orchestrating
semantic
web
systems,
will
be
driving
force
bring
knowledge,
evolving
our
way
automating
laboratory.
Journal of the American Chemical Society,
Journal Year:
2023,
Volume and Issue:
145(40), P. 21699 - 21716
Published: Sept. 27, 2023
Exceptional
molecules
and
materials
with
one
or
more
extraordinary
properties
are
both
technologically
valuable
fundamentally
interesting,
because
they
often
involve
new
physical
phenomena
compositions
that
defy
expectations.
Historically,
exceptionality
has
been
achieved
through
serendipity,
but
recently,
machine
learning
(ML)
automated
experimentation
have
widely
proposed
to
accelerate
target
identification
synthesis
planning.
In
this
Perspective,
we
argue
the
data-driven
methods
commonly
used
today
well-suited
for
optimization
not
realization
of
exceptional
molecules.
Finding
such
outliers
should
be
possible
using
ML,
only
by
shifting
away
from
traditional
ML
approaches
tweak
composition,
crystal
structure,
reaction
pathway.
We
highlight
case
studies
high-Tc
oxide
superconductors
superhard
demonstrate
challenges
ML-guided
discovery
discuss
limitations
automation
task.
then
provide
six
recommendations
development
capable
discovery:
(i)
Avoid
tyranny
middle
focus
on
extrema;
(ii)
When
data
limited,
qualitative
predictions
direction
than
interpolative
accuracy;
(iii)
Sample
what
can
made
how
make
it
defer
optimization;
(iv)
Create
room
(and
look)
unexpected
while
pursuing
your
goal;
(v)
Try
fill-in-the-blanks
input
output
space;
(vi)
Do
confuse
human
understanding
model
interpretability.
conclude
a
description
these
integrated
into
workflows,
which
enable
materials.
Chemical Reviews,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 2, 2025
Two-dimensional
transition
metal
dichalcogenides
(2D
TMDs)
are
a
promising
class
of
functional
materials
for
fundamental
physics
explorations
and
applications
in
next-generation
electronics,
catalysis,
quantum
technologies,
energy-related
fields.
Theory
simulations
have
played
pivotal
role
recent
advancements,
from
understanding
physical
properties
discovering
new
to
elucidating
synthesis
processes
designing
novel
devices.
The
key
has
been
developments
ab
initio
theory,
deep
learning,
molecular
dynamics,
high-throughput
computations,
multiscale
methods.
This
review
focuses
on
how
theory
contributed
progress
2D
TMDs
research,
particularly
twisted
moiré-based
TMDs,
predicting
exotic
phases
TMD
monolayers
heterostructures,
nucleation
growth
synthesis,
comprehending
electron
transport
characteristics
different
contacts
potential
devices
based
heterostructures.
notable
achievements
provided
by
highlighted,
along
with
the
challenges
that
need
be
addressed.
Although
demonstrated
prototype
created,
we
conclude
highlighting
research
areas
demand
most
attention
simulation
might
address
them
aid
attaining
true
toward
commercial
device
realizations.
Chemistry - Methods,
Journal Year:
2021,
Volume and Issue:
1(11), P. 454 - 467
Published: Sept. 13, 2021
Abstract
Flow
chemistry
studies
can
sometimes
be
difficult
to
reproduce.
In
this
article
we
provide
guidance
scientists
for
experimental
details
that
should
considered
as
part
of
any
organic
chemistry‐based
continuous
flow
study.
A
focus
is
placed
on
information
provided
within
reported
enable
experiments
more
easily
and
reliably
reproduced.
Topics
covered
include
reactor
components
assembly,
important
parameter
effects
useful
performance
criteria.
The
covers
aspects
homogeneous
systems,
multiphase
transformations,
catalytic
reactions
(homogeneous
heterogeneous).
detailed
discussion
photochemistry,
biocatalysis
electrochemical
systems
outside
the
scope
review.
Advanced Science,
Journal Year:
2022,
Volume and Issue:
9(10)
Published: Feb. 1, 2022
Autonomous
flow
reactors
are
becoming
increasingly
utilized
in
the
synthesis
of
organic
compounds,
yet
complexity
chemical
reactions
and
analytical
methods
remains
limited.
The
development
a
modular
platform
which
uses
rapid
NMR
FTIR
measurements,
combined
with
chemometric
modeling,
is
presented
for
efficient
timely
analysis
reaction
outcomes.
This
tested
four
variable
single-step
(nucleophilic
aromatic
substitution),
to
determine
most
effective
optimization
methodology.
self-optimization
approach
minimal
background
knowledge
proves
provide
optimal
parameters
within
shortest
operational
time.
chosen
then
applied
seven
two-step
problem
(imine
formation
cyclization),
active
pharmaceutical
ingredient
edaravone.
Despite
exponentially
increased
this
problem,
achieves
excellent
results
relatively
small
number
iterations,
leading
>95%
solution
yield
intermediate
up
5.42
kg
L
