Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 28, 2024
Methods
that
accelerate
the
evaluation
of
molecular
properties
are
essential
for
chemical
discovery.
While
some
degree
ligand
additivity
has
been
established
transition
metal
complexes,
it
is
underutilized
in
asymmetric
such
as
square
pyramidal
coordination
geometries
highly
relevant
to
catalysis.
To
develop
predictive
methods
beyond
simple
additivity,
we
apply
a
many-body
expansion
octahedral
and
complexes
introduce
correction
based
on
adjacent
ligands
(i.e.,
cis
interaction
model).
We
first
test
model
adiabatic
spin-splitting
energies
Fe(II)
predicting
DFT-calculated
values
unseen
binary
within
an
average
error
1.4
kcal/mol.
Uncertainty
analysis
reveals
optimal
basis,
comprising
homoleptic
mer
symmetric
complexes.
next
show
solved
basis)
infers
both
DFT-
CCSD(T)-calculated
catalytic
reaction
1
kcal/mol
average.
The
predicts
low-symmetry
with
outside
range
complex
energies.
observe
trans
interactions
unnecessary
most
monodentate
systems
but
can
be
important
combinations
ligands,
containing
mixture
bidentate
ligands.
Finally,
demonstrate
may
combined
Δ-learning
predict
CCSD(T)
from
exhaustively
calculated
DFT
same
fraction
needed
model,
achieving
around
30%
using
alone.
In
this
study,
we
explore
the
impact
of
alkali
metal
ions
(Li+,
Na+,
K+,
Rb+,
and
Cs+)
on
hydration
structure
water
using
molecular
dynamics
simulations
carried
out
with
MB-nrg
potential
energy
functions
(PEFs).
Our
analyses
include
radial
distribution
functions,
coordination
numbers,
dipole
moments,
infrared
spectra
molecules,
calculated
as
a
function
solvation
shells.
The
results
collectively
indicate
highly
local
influence
all
hydrogen-bond
network
established
by
surrounding
smallest
most
densely
charged
Li+
ion
exerting
pronounced
effect.
Remarkably,
PEFs
demonstrate
excellent
agreement
available
experimental
data
for
position
size
first
shells,
underscoring
their
predictive
models
realistic
ionic
aqueous
solutions
across
various
thermodynamic
conditions
environments.
The Journal of Physical Chemistry A,
Год журнала:
2024,
Номер
128(9), С. 1600 - 1610
Опубликована: Фев. 23, 2024
Path
integral
(PI)
simulations
are
used
to
explore
nuclear
quantum
effects
(NQEs)
in
hydroxide
hydrate
and
its
perdeuterated
isotopomer
along
the
H-bond
bifurcation
pathway.
Toward
this,
a
new
potential
energy
surface
using
symmetric
gradient
domain
machine
learning
method
with
ab
initio
data
at
CCSD(T)/aug-cc-pVTZ
level
is
built.
From
PI
umbrella
sampling
(US)
simulations,
free
profiles
coordinate
explored
as
function
of
temperature.
At
ambient
temperature,
barrier
increased
upon
inclusion
NQEs.
low
temperatures
deep
tunneling
regime,
strongly
decreased
flattened.
These
trends
examined,
role
O–O
distance
also
investigated
through
two-dimensional
US
simulations.
The Journal of Physical Chemistry Letters,
Год журнала:
2024,
Номер
15(15), С. 4070 - 4075
Опубликована: Апрель 8, 2024
Nuclear
quantum
effects
play
an
important
role
in
the
structure
and
thermodynamics
of
aqueous
systems.
By
performing
a
many-body
expansion
with
nuclear-electronic
orbital
(NEO)
theory,
we
show
that
proton
quantization
can
give
rise
to
significant
energetic
contributions
for
interactions
spanning
several
molecules
single-point
energy
calculations
water
clusters.
Although
zero-point
motion
produces
large
increase
at
one-body
level,
nuclear
serve
stabilize
higher-order
molecular
interactions.
These
results
are
because
they
demonstrate
nontrivial
Our
approach
also
provides
pathway
incorporating
into
potential
surfaces.
The
NEO
is
advantageous
analyses
it
includes
directly
energies.
Developing
a
molecular-level
understanding
of
the
properties
water
is
central
to
numerous
scientific
and
technological
applications.
However,
accurately
modeling
through
computer
simulations
has
been
significant
challenge
due
complex
nature
hydrogen-
bonding
network
that
molecules
form
under
different
thermodynamic
conditions.
This
complexity
led
over
five
decades
research
many
attempts.
The
introduction
MB-pol
data-driven
many-body
potential
energy
function
marked
advancement
toward
universal
molecular
model
capable
predicting
structural,
thermo-
dynamic,
dynamical,
spectroscopic
across
all
phases.
By
integrating
physics-based
(i.e.,
machine-learned)
components,
which
correctly
capture
delicate
balance
among
interactions,
achieves
chemical
accuracy,
enabling
realistic
water,
from
gas-phase
clusters
liquid
ice.
In
this
review,
we
present
comprehensive
overview
data-
driven
formalism
adopted
by
MB-pol,
highlight
main
results
predictions
made
with
date,
discuss
prospects
for
future
extensions
potentials
generic
reactive
systems.
Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
unknown
Опубликована: Ноя. 28, 2024
Methods
that
accelerate
the
evaluation
of
molecular
properties
are
essential
for
chemical
discovery.
While
some
degree
ligand
additivity
has
been
established
transition
metal
complexes,
it
is
underutilized
in
asymmetric
such
as
square
pyramidal
coordination
geometries
highly
relevant
to
catalysis.
To
develop
predictive
methods
beyond
simple
additivity,
we
apply
a
many-body
expansion
octahedral
and
complexes
introduce
correction
based
on
adjacent
ligands
(i.e.,
cis
interaction
model).
We
first
test
model
adiabatic
spin-splitting
energies
Fe(II)
predicting
DFT-calculated
values
unseen
binary
within
an
average
error
1.4
kcal/mol.
Uncertainty
analysis
reveals
optimal
basis,
comprising
homoleptic
mer
symmetric
complexes.
next
show
solved
basis)
infers
both
DFT-
CCSD(T)-calculated
catalytic
reaction
1
kcal/mol
average.
The
predicts
low-symmetry
with
outside
range
complex
energies.
observe
trans
interactions
unnecessary
most
monodentate
systems
but
can
be
important
combinations
ligands,
containing
mixture
bidentate
ligands.
Finally,
demonstrate
may
combined
Δ-learning
predict
CCSD(T)
from
exhaustively
calculated
DFT
same
fraction
needed
model,
achieving
around
30%
using
alone.
