Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(5), P. 3458 - 3470
Published: Jan. 25, 2024
Ligand
modulation
of
transition-metal
catalysts
to
achieve
optimal
reactivity
and
selectivity
in
alkene
hydrofunctionalization
is
a
fundamental
challenge
synthetic
organic
chemistry.
Hydroaminoalkylation,
an
atom-economical
approach
for
alkylating
amines
using
alkenes,
particularly
significant
amine
synthesis
the
pharmaceutical,
agrochemical,
fine
chemical
industries.
However,
existing
methods
usually
require
specific
substrate
combinations
precise
regio-
stereoselectivity,
which
limits
their
practical
utility.
Protocols
allowing
regiodivergent
hydroaminoalkylation
from
same
starting
materials,
controlling
both
regiochemical
stereochemical
outcomes,
are
currently
absent.
Herein,
we
report
ligand-controlled,
nickel-catalyzed
unactivated
alkenes
with
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(12), P. 8536 - 8546
Published: March 13, 2024
Methods
to
access
chiral
sulfur(VI)
pharmacophores
are
of
interest
in
medicinal
and
synthetic
chemistry.
We
report
the
desymmetrization
unprotected
sulfonimidamides
via
asymmetric
acylation
with
a
cinchona-phosphinate
catalyst.
The
desired
products
formed
excellent
yield
enantioselectivity
no
observed
bis-acylation.
A
data-science-driven
approach
substrate
scope
evaluation
was
coupled
high
throughput
experimentation
(HTE)
facilitate
statistical
modeling
order
inform
mechanistic
studies.
Reaction
kinetics,
catalyst
structural
studies,
density
functional
theory
(DFT)
transition
state
analysis
elucidated
turnover-limiting
step
be
collapse
tetrahedral
intermediate
provided
key
insights
into
catalyst-substrate
structure–activity
relationships
responsible
for
origin
enantioselectivity.
This
study
offers
reliable
method
accessing
enantioenriched
propel
their
application
as
serves
an
example
insight
that
can
gleaned
from
integrating
data
science
traditional
physical
organic
techniques.
Precision Chemistry,
Journal Year:
2024,
Volume and Issue:
2(12), P. 612 - 627
Published: Sept. 14, 2024
Atomic
simulations
aim
to
understand
and
predict
complex
physical
phenomena,
the
success
of
which
relies
largely
on
accuracy
potential
energy
surface
description
efficiency
capture
important
rare
events.
LASP
software
(large-scale
atomic
simulation
with
a
Neural
Network
Potential),
released
in
2018,
incorporates
key
ingredients
fulfill
ultimate
goal
by
combining
advanced
neural
network
potentials
efficient
global
optimization
methods.
This
review
introduces
recent
development
along
two
main
streams,
namely,
higher
intelligence
more
automation,
solve
material
reaction
problems.
The
latest
version
(LASP
3.7)
features
many-body
function
corrected
(G-MBNN)
improve
PES
low
cost,
achieves
linear
scaling
for
large-scale
simulations.
functionalities
are
updated
incorporate
(i)
ASOP
ML-interface
methods
finding
interface
structures
under
grand
canonic
conditions;
(ii)
ML-TS
MMLPS
identify
lowest
pathway.
With
these
powerful
functionalities,
now
serves
as
an
intelligent
data
generator
create
computational
databases
end
users.
We
exemplify
database
construction
zeolite
metal-ligand
properties
new
catalyst
design.
ChemCatChem,
Journal Year:
2024,
Volume and Issue:
16(10)
Published: Jan. 5, 2024
Abstract
Significant
progress
has
been
made
in
recent
years
the
use
of
AI
and
Machine
Learning
(ML)
for
catalyst
discovery
optimisation.
The
effectiveness
ML
data
science
techniques
was
demonstrated
predicting
optimising
enantioselectivity
regioselectivity
catalytic
reactions
through
optimisation
ligands,
counterions
reaction
conditions.
Direct
new
catalysts/reactions
is
more
difficult
requires
efficient
exploration
transition
metal
chemical
space.
A
range
computational
descriptor
generation,
ranging
from
molecular
mechanics
to
DFT
methods,
have
successfully
demonstrated,
often
conjunction
with
reduce
cost
associated
TS
calculations.
Complex
aspects
reactions,
such
as
solvent,
temperature,
etc.,
also
incorporated
into
workflow.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(5), P. 3458 - 3470
Published: Jan. 25, 2024
Ligand
modulation
of
transition-metal
catalysts
to
achieve
optimal
reactivity
and
selectivity
in
alkene
hydrofunctionalization
is
a
fundamental
challenge
synthetic
organic
chemistry.
Hydroaminoalkylation,
an
atom-economical
approach
for
alkylating
amines
using
alkenes,
particularly
significant
amine
synthesis
the
pharmaceutical,
agrochemical,
fine
chemical
industries.
However,
existing
methods
usually
require
specific
substrate
combinations
precise
regio-
stereoselectivity,
which
limits
their
practical
utility.
Protocols
allowing
regiodivergent
hydroaminoalkylation
from
same
starting
materials,
controlling
both
regiochemical
stereochemical
outcomes,
are
currently
absent.
Herein,
we
report
ligand-controlled,
nickel-catalyzed
unactivated
alkenes
with
