Developing a Vial-Scale Methodology for the Measurement of Nucleation Kinetics Using Evaporative Crystallization: A Case Study with Sodium Chloride
Crystal Growth & Design,
Journal Year:
2025,
Volume and Issue:
unknown
Published: April 4, 2025
Understanding
nucleation
kinetics
is
vital
for
designing
crystallization
processes,
yet
traditional
measurement
methods
based
on
cooling
are
unsuitable
compounds
with
temperature-independent
solubility.
This
study
introduces
an
experimental
procedure
to
measure
the
evaporative
and
applies
it
sodium
chloride
(NaCl)
in
water.
By
systematically
varying
conditions
such
as
temperature
evaporation
gas
flow
rate,
we
obtained
a
comprehensive
data
set
of
NaCl
crystals
that
allowed
estimating
kinetic
parameters
using
rate
expression
derived
from
classical
theory
(CNT).
work
demonstrates
robustness
method
measuring
applicable
regardless
how
solubility
compound
depends
temperature.
Language: Английский
Machine Learning Nucleation Collective Variables with Graph Neural Networks
Journal of Chemical Theory and Computation,
Journal Year:
2023,
Volume and Issue:
20(4), P. 1600 - 1611
Published: Oct. 25, 2023
The
efficient
calculation
of
nucleation
collective
variables
(CVs)
is
one
the
main
limitations
to
application
enhanced
sampling
methods
investigation
processes
in
realistic
environments.
Here
we
discuss
development
a
graph-based
model
for
approximation
CVs
that
enables
orders-of-magnitude
gains
computational
efficiency
on-the-fly
evaluation
CVs.
By
performing
simulations
on
nucleating
colloidal
system
mimicking
multistep
process
from
solution,
assess
model's
both
postprocessing
and
biasing
trajectories
with
pulling,
umbrella
sampling,
metadynamics
simulations.
Moreover,
probe
transferability
models
across
systems
using
CV
based
sixth-order
Steinhardt
parameters
trained
drive
crystalline
copper
its
melt.
Our
approach
general
potentially
transferable
more
complex
as
well
different
Language: Английский
Exploring Carbamazepine Polymorph Crystal Growth in Water by Enhanced Sampling Simulations
ACS Omega,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Aug. 15, 2024
In
this
work,
the
polymorphism
of
active
pharmaceutical
ingredient
carbamazepine
(CBZ)
was
investigated
by
using
molecular
dynamics
simulations
with
an
enhanced
sampling
scheme.
A
single
molecule
CBZ
attaching
to
flat
surfaces
different
polymorphs
used
as
a
model
for
secondary
nucleation
in
water.
novel
approach
developed
compute
free
energy
profile
characterizing
adsorption
molecules
orientation
aligned
crystal
structure
surface.
The
distribution
states
that
showed
alignment
rescale
include
only
contribution
is
consistent
growth.
resulting
favorable
thermodynamics
most
stable
form,
Form
III
and
second
I.
primary
crystallization
product,
dihydrate,
found
be
less
favorable,
implying
nonclassical
pathway.
We
suggest
major
determining
energetics
hydrophobicity
This
thermodynamic
ranking
provides
valuable
information
about
pathways
polymorph
growth
will
further
contribute
understanding
process
CBZ,
which
imperative
since
formation
can
alter
physical
properties
drug
significantly.
Language: Английский
Machine Learning Nucleation Collective Variables with Graph Neural Networks
Published: Oct. 4, 2023
The
efficient
calculation
of
nucleation
collective
variables
(CVs)
is
one
the
main
limitations
to
application
enhanced
sampling
methods
investigation
processes
in
realistic
environments.
Here
we
discuss
development
a
graph-based
model
for
approximation
CVs,
which
enables
orders-of-magnitude
gains
computational
efficiency
on-the-fly
evaluation
CVs.
By
performing
simulations
on
nucleating
colloidal
system
mimicking
multistep
process
from
solution,
assess
model's
both
postprocessing
and
biasing
trajectories
with
pulling,
umbrella
metadynamics
simulations.
Moreover,
probe
transferability
models
CVs
across
systems
by
using
CV
based
sixth-order
Steinhardt
parameters
trained
drive
crystalline
copper
its
melt.
Our
approach
general
potentially
transferable
more
complex
as
well
different
Language: Английский
Machine Learning Nucleation Collective Variables with Graph Neural Networks
Published: June 30, 2023
The
efficient
calculation
of
nucleation
collective
variables
(CVs)
is
one
the
main
limitations
to
ap-
plication
enhanced
sampling
methods
investigation
processes
in
realistic
environments.
Here
we
discuss
development
a
graph-based
model
for
approximation
CVs,
which
en-
ables
orders-of-magnitude
gains
computational
efficiency
on-the-fly
evaluation
CVs.
By
performing
simulations
on
nucleating
colloidal
system,
assess
model’s
both
postprocessing
and
biasing
trajectories,
thereby
mimicking
multistep
process
from
solu-
tion.
Moreover,
probe
transferability
graph
approximations
across
systems
by
using
CV
based
sixth-order
Steinhardt
parameters.
was
trained
with
data
collected
system
used
drive
crystalline
copper
its
melt.
Our
approach
general
fully
transferable
more
complex
as
well
different
Language: Английский
Machine Learning Nucleation Collective Variables with Graph Neural Networks
Published: Oct. 6, 2023
The
efficient
calculation
of
nucleation
collective
variables
(CVs)
is
one
the
main
limitations
to
application
enhanced
sampling
methods
investigation
processes
in
realistic
environments.
Here
we
discuss
development
a
graph-based
model
for
approximation
CVs,
which
enables
orders-of-magnitude
gains
computational
efficiency
on-the-fly
evaluation
CVs.
By
performing
simulations
on
nucleating
colloidal
system
mimicking
multistep
process
from
solution,
assess
model's
both
postprocessing
and
biasing
trajectories
with
pulling,
umbrella
metadynamics
simulations.
Moreover,
probe
transferability
models
CVs
across
systems
by
using
CV
based
sixth-order
Steinhardt
parameters
trained
drive
crystalline
copper
its
melt.
Our
approach
general
potentially
transferable
more
complex
as
well
different
Language: Английский