Insight into melting point differences of dinitroimidazoles and dinitropyrazoles from the perspective of intermolecular interactions
Physical Chemistry Chemical Physics,
Год журнала:
2024,
Номер
26(5), С. 4752 - 4758
Опубликована: Янв. 1, 2024
A
linear
equation
relating
the
interaction
energy
and
melting
point
was
fitted
by
decomposing
periodic
crystal
structures
into
molecular
dimers
calculating
their
energies
using
Symmetry-Adapted
Perturbation
Theory
(SAPT).
Язык: Английский
A physical organic strategy to predict and interpret stabilities of chemical bonds in energetic compounds for the discovery of thermal-resistant properties
Journal of Molecular Modeling,
Год журнала:
2024,
Номер
30(3)
Опубликована: Фев. 26, 2024
Язык: Английский
Standardizing differential scanning calorimetry (DSC) thermal decomposition temperatures at various heating rates of an energetic material as a threshold one
Energetic Materials Frontiers,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 1, 2024
Differential
scanning
calorimetry
(DSC)
test
is
capable
of
providing
comprehensive
data
peak
temperature
(,
K)
and
onset
at
various
heating
rates
(β)
widely
applied
in
the
thermal
safety
assessment
energetic
materials
(EMs).
However,
()
are
variable,
depending
on
β,
making
inconvenience
confusion
stability
different
EMs,
particular,
case
testing
conditions
absent.
This
study
aims
to
standardize
β
as
a
threshold
decomposition.
It
confirmed
that
Pow2P2
(two-parameter
power
function)
feasible
fit
relationship
by
any
two
experimental
points,
extrapolate
.
Thereby,
,
single
value
DSC
one
EM,
benefits
for
study.
Язык: Английский
Screening heat-resistant energetic molecules via deep learning and high-throughput computation
Chemical Engineering Journal,
Год журнала:
2025,
Номер
unknown, С. 160218 - 160218
Опубликована: Фев. 1, 2025
Язык: Английский
Interpretable and Physicochemical-Intuitive Deep Learning Approach for the Design of Thermal Resistance of Energetic Compounds
The Journal of Physical Chemistry A,
Год журнала:
2024,
Номер
128(41), С. 9045 - 9054
Опубликована: Окт. 8, 2024
Thermal
resistance
of
energetic
materials
is
critical
due
to
its
impact
on
safety
and
sustainability.
However,
developing
predictive
models
remains
challenging
because
data
scarcity
limited
insights
into
quantitative
structure–property
relationships.
In
this
work,
a
deep
learning
framework,
named
EM-thermo,
was
proposed
address
these
challenges.
A
set
comprising
5029
CHNO
compounds,
including
976
constructed
facilitate
study.
EM-thermo
employs
molecular
graphs
direct
message-passing
neural
networks
capture
structural
features
predict
thermal
resistance.
Using
transfer
learning,
the
model
achieves
an
accuracy
approximately
97%
for
predicting
thermal-resistance
property
(decomposition
temperatures
above
573.15
K)
in
compounds.
The
involvement
descriptors
improved
prediction.
These
findings
suggest
that
effective
correlating
from
atom
covalent
bond
level,
offering
promising
tool
advancing
design
discovery
field
Язык: Английский
A Physical Organic Strategy to Predict and Interpret Stabilities of Chemical Bonds in Energetic Compounds for the Discovery of Thermal-Resistant Properties
Research Square (Research Square),
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
Abstract
The
in-depth
understanding
about
the
stability
of
chemical
bonds
in
energetic
compounds
plays
a
central
role
for
molecular
design
and
safety-related
evaluations.
Most
contain
nitro
as
explosophores,
cleavage
is
fundamental
thermal
mechanical
stability.
However,
quantum
chemistry
approach
to
accurately
predict
energy
temperature
properties
related
bond
challenging,
due
tradeoff
between
computational
costs
deviations.
Herein,
orders
are
proposed
accurate
computational-cost
efficient
descriptors
predicting
thermal-resistant
properties.
intrinsic
strength
index
(IBSI)
demonstrates
best
prediction
experimental
homolytic
dissociation
energies
(R
2
>
0.996),
which
on
par
with
results
from
high-precision
methods.
effects
connectivity
steric
hindrance
hierarchy
were
analyzed
reveal
underlying
mechanisms.
Additionally,
IBSI
successfully
applied
decomposition
temperatures
24
heat-resistant
=
0.995),
thus
validating
effectiveness
interpretation
via
physical
organic
approach.
Язык: Английский
Quantitatively Determining Melting Properties for Energetic Compounds Via Knowledge-Infused Molecular Graphs and Interpretable Deep Learning
Опубликована: Янв. 1, 2024
The
melting
properties
of
energetic
compounds
are
critical
to
their
performances,
but
challenges
persist
in
understanding
the
molecular
features
and
design
strategies
that
drive
these
properties.
Integrating
domain
knowledge
into
data-driven
approaches
for
predicting
enhances
generation
comprehensive
insights
enables
construction
interpretable
prediction
models.
For
this
purpose,
a
knowledge-infused
graphs
(KIMGs)
were
devised
describe
characters
compounds,
by
which
models
developed
conjunction
with
message
passing
neural
networks.
A
melting-point
dataset
composed
around
30,000
melt-castable
was
constructed,
collection
29
key
descriptors
relevant
behaviors
is
integrated
KIMGs.
model
achieved
best
mean
absolute
error
10.93
K
point
prediction.
interpretability
from
both
feature
importances
offered
complex
interplay
determine
compounds.
This
work
researchers
not
only
predict
enhanced
accuracy
also
applicable
establishing
other
quantitively
structure-property
relationships.
Язык: Английский
Quantitatively determining melting properties for energetic compounds via knowledge-infused molecular graphs and interpretable deep learning
Energetic Materials Frontiers,
Год журнала:
2024,
Номер
unknown
Опубликована: Сен. 1, 2024
Язык: Английский
Accelerating the Design of High-Energy-Density Hydrocarbon Fuels by Learning from the Data
Molecules,
Год журнала:
2023,
Номер
28(21), С. 7361 - 7361
Опубликована: Окт. 31, 2023
In
the
ZINC20
database,
with
aid
of
maximum
substructure
searches,
common
substructures
were
obtained
from
molecules
high-strain-energy
and
combustion
heat
values,
further
provided
domain
knowledge
on
how
to
design
high-energy-density
hydrocarbon
(HEDH)
fuels.
Notably,
quadricyclane
syntin
could
be
topologically
assembled
through
these
substructures,
corresponding
schemes
guided
20
fuel
(ZD-1
ZD-20).
The
properties
evaluated
by
using
group-contribution
methods
density
functional
theory
(DFT)
calculations,
where
ZD-6
stood
out
due
high
volumetric
net
combustion,
specific
impulse,
low
melting
point,
acceptable
flash
point.
Based
neural
network
model
for
evaluating
synthetic
complexity
(SCScore),
estimated
value
was
close
that
syntin,
indicating
comparable
syntin.
This
work
not
only
provides
as
a
potential
HEDH
fuel,
but
also
illustrates
superiority
learning
strategies
data
in
increasing
understanding
structure
performance
relationships
accelerating
development
novel
Язык: Английский