The Journal of Physical Chemistry A,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 25, 2025
Machine
learning
has
recently
been
introduced
into
the
arsenal
of
tools
that
are
available
to
computational
chemists.
In
past
few
years,
we
have
seen
an
increase
in
applicability
these
on
a
plethora
applications,
including
automated
exploration
large
fraction
chemical
space,
reduction
repetitive
tasks,
detection
outliers
databases,
and
acceleration
molecular
simulations.
An
attractive
application
machine
electronic
structure
theory
is
"recycling"
wave
functions
for
faster
more
accurate
completion
complex
quantum
calculations.
Along
lines,
developed
hybrid
chemical/machine
workflows
utilize
information
from
low-level
prediction
higher-level
functions.
The
data-driven
coupled-cluster
(DDCC)
family
methods
discussed
this
article
together
with
importance
inclusion
physical
properties
such
workflows.
After
short
introduction
philosophy
capabilities
DDCC,
present
our
recent
progress
extending
its
larger
structures
data
sets.
A
significant
advantage
offered
by
DDCC
transferability,
respect
different
systems
excitation
levels.
As
show
here,
predicted
at
singles
doubles
level
can
be
used
perturbative
triples
CCSD(T)
scheme.
We
conclude
some
personal
considerations
future
directions
related
development
next
generation
models.
The Journal of Organic Chemistry,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 6, 2025
This
study
investigates
the
Cope
elimination
reaction,
focusing
on
mechanistic
shift
between
concerted
and
stepwise
pathways
influenced
by
substituent
effects.
Experimental
approaches,
including
kinetic
isotope
effects
(KIEs)
linear
free
energy
relationships
(LFERs),
alongside
density
functional
theory
(DFT)
computations,
were
employed
to
explore
influence
of
substituents
reaction
kinetics
pathways.
Our
findings
reveal
temperature-
substituent-dependent
partitioning
syn-β
E1cB
mechanism,
providing
deeper
insights
into
diversity
reactions.
ACS Catalysis,
Journal Year:
2025,
Volume and Issue:
15(3), P. 2110 - 2123
Published: Jan. 22, 2025
Enantioselective
C(sp3)-H
bond
oxidation
is
a
powerful
strategy
for
installing
functionality
in
rich
molecules.
Site-
and
enantioselective
of
strong
C-H
bonds
monosubstituted
cyclohexanes
with
hydrogen
peroxide
catalyzed
by
aminopyridine
manganese
catalysts
combination
alkanoic
acids
has
been
recently
described.
Mechanistic
uncertainties
nonobvious
enantioselectivity
trends
challenge
development
the
full
potential
this
reaction
as
synthetic
tool.
Herein,
we
apply
predictive
statistical
analysis
to
identify
mechanistically
informative
correlations
that
provide
valuable
understanding
will
guide
future
optimization
reactions.
Chemical Science,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 1, 2025
This
review
provides
an
overview
of
predictive
tools
in
asymmetric
synthesis.
The
evolution
methods
from
simple
qualitative
pictures
to
complicated
quantitative
approaches
is
connected
with
the
increased
complexity
stereoselective
The Journal of Physical Chemistry A,
Journal Year:
2025,
Volume and Issue:
unknown
Published: March 25, 2025
Machine
learning
has
recently
been
introduced
into
the
arsenal
of
tools
that
are
available
to
computational
chemists.
In
past
few
years,
we
have
seen
an
increase
in
applicability
these
on
a
plethora
applications,
including
automated
exploration
large
fraction
chemical
space,
reduction
repetitive
tasks,
detection
outliers
databases,
and
acceleration
molecular
simulations.
An
attractive
application
machine
electronic
structure
theory
is
"recycling"
wave
functions
for
faster
more
accurate
completion
complex
quantum
calculations.
Along
lines,
developed
hybrid
chemical/machine
workflows
utilize
information
from
low-level
prediction
higher-level
functions.
The
data-driven
coupled-cluster
(DDCC)
family
methods
discussed
this
article
together
with
importance
inclusion
physical
properties
such
workflows.
After
short
introduction
philosophy
capabilities
DDCC,
present
our
recent
progress
extending
its
larger
structures
data
sets.
A
significant
advantage
offered
by
DDCC
transferability,
respect
different
systems
excitation
levels.
As
show
here,
predicted
at
singles
doubles
level
can
be
used
perturbative
triples
CCSD(T)
scheme.
We
conclude
some
personal
considerations
future
directions
related
development
next
generation
models.
