Abstract.
Sulfuric
acid,
ammonia,
and
amines
are
believed
to
be
key
contributors
the
initial
steps
in
new
particle
formation
atmosphere.
However,
other
compounds
such
as
organic
or
nitric
acid
important
for
further
growth
at
larger
sizes.
In
this
study,
we
investigate
potential
uptake
of
first-generation
oxidation
products
from
α-pinene
(pinic
pinonic
acid),
isoprene
(trans-β-IEPOX,
β4-ISPOOH,
β1-ISOPOH),
a
highly
oxidized
molecule
(HOM),
formic
acid.
The
is
probed
onto
(SA)10(base)10
freshly
nucleated
particles
(FNPs),
where
SA
denotes
sulfuric
bases
either
ammonia
(AM),
methylamine
(MA),
dimethylamine
(DMA),
trimethylamine
(TMA).
addition
free
energies
were
calculated
ωB97X-D3BJ/6-311++G(3df,3pd)//B97-3c
level
theory.
We
find
favorable
−8
−10
kcal/mol
HOM,
pinic
on
less
sterically
hindered
(SA)10(AM)10
(SA)10(MA)10
FNPs.
This
suggests
that
do
not
contribute
early
FNPs,
but
do,
accordance
with
their
expected
volatilities.
Calculating
second
maintains
its
large
energy
decrease
due
two
carboxylic
groups
interacting
monomer
well
FNP.
drops
−3.9
weak
interactions
between
FNP
carbonyl
group
lack
monomer–monomer
interactions.
high
confirmed
by
calculating
realistic
atmospheric
conditions.
means
has
∼2
nm
implying
dicarboxylic
acids
could
potentially
also
aid
growth.
Chemical Physics Reviews,
Journal Year:
2023,
Volume and Issue:
4(3)
Published: Sept. 1, 2023
Atmospheric
molecular
cluster
formation
is
the
first
stage
toward
aerosol
particle
formation.
Despite
intensive
progress
in
recent
years,
relative
role
of
different
vapors
and
mechanisms
for
forming
clusters
still
not
well-understood.
Quantum
chemical
(QC)
methods
can
give
insight
into
thereby
yield
information
about
potentially
relevant
compounds.
Here,
we
summarize
QC
literature
on
clustering
involving
species
such
as
sulfuric
acid,
methanesulfonic
nitric
acid.
The
importance
iodine
iodous
acid
(HIO2)
iodic
(HIO3)
atmospheric
an
emerging
topic,
critically
review
our
view
how
to
future.
We
outline
machine
learning
(ML)
be
used
enhance
configurational
sampling,
leading
a
massive
increase
compositions
that
modeled.
In
future,
ML-boosted
could
allow
us
comprehensively
understand
complex
with
multiple
pathways,
one
step
closer
implementing
accurate
models.
ACS Omega,
Journal Year:
2023,
Volume and Issue:
8(28), P. 25155 - 25164
Published: June 30, 2023
Formation
and
growth
of
atmospheric
molecular
clusters
into
aerosol
particles
impact
the
global
climate
contribute
to
high
uncertainty
in
modern
models.
Cluster
formation
is
usually
studied
using
quantum
chemical
methods,
which
quickly
becomes
computationally
expensive
when
system
sizes
grow.
In
this
work,
we
present
a
large
database
∼250k
relevant
cluster
structures,
can
be
applied
for
developing
machine
learning
(ML)
The
used
train
ML
model
kernel
ridge
regression
(KRR)
with
FCHL19
representation.
We
test
ability
extrapolate
from
smaller
larger
clusters,
between
different
molecules,
equilibrium
structures
out-of-equilibrium
transferability
onto
systems
new
interactions.
show
that
KRR
models
transfer
acid
base
interactions
mean
absolute
errors
below
1
kcal/mol.
suggest
introducing
an
iterative
step
configurational
sampling
processes,
reduce
computational
expense.
Such
approach
would
allow
us
study
significantly
more
at
higher
accuracy
than
previously
possible
thereby
cover
much
part
compounds.
The Journal of Physical Chemistry A,
Journal Year:
2023,
Volume and Issue:
127(36), P. 7568 - 7578
Published: Aug. 31, 2023
Multicomponent
atmospheric
molecular
clusters,
typically
comprising
a
combination
of
acids
and
bases,
play
pivotal
role
in
our
climate
system
contribute
to
the
perplexing
uncertainties
embedded
modern
models.
Our
understanding
cluster
formation
is
limited
by
lack
studies
on
complex
mixed-acid-mixed-base
systems.
Here,
we
investigate
multicomponent
clusters
consisting
mixtures
several
acid
base
molecules:
sulfuric
(SA),
methanesulfonic
(MSA),
nitric
(NA),
formic
(FA),
along
with
methylamine
(MA),
dimethylamine
(DMA),
trimethylamine
(TMA).
We
calculated
binding
free
energies
comprehensive
set
252
at
DLPNO-CCSD(T0)/aug-cc-pVTZ//ωB97X-D/6-31++G(d,p)
level
theory.
Combined
existing
datasets,
simulated
new
particle
(NPF)
rates
using
Atmospheric
Cluster
Dynamics
Code
(ACDC).
find
that
presence
NA
FA
had
substantial
impact,
increasing
NPF
rate
60%
realistic
conditions.
Intriguingly,
suppress
MSA
NPF.
These
findings
suggest
even
high
concentration
has
impact
polluted
regions
NA.
outline
method
for
generating
lookup
table
could
potentially
be
used
models
sufficiently
incorporates
all
required
chemistry.
By
unraveling
mechanisms
get
one
step
closer
comprehending
their
implications
global
system.
Aerosol Research,
Journal Year:
2024,
Volume and Issue:
2(1), P. 123 - 134
Published: June 4, 2024
Abstract.
The
role
of
organic
compounds
in
atmospheric
new
particle
formation
is
difficult
to
disentangle
due
the
myriad
potentially
important
oxygenated
molecules
(OOMs)
present
atmosphere.
Using
state-of-the-art
quantum
chemical
methods,
we
here
employ
a
novel
approach,
denoted
“cluster-of-functional-groups”
for
studying
involvement
OOMs
cluster
formation.
Instead
usual
“trial-and-error”
approach
testing
ability
experimentally
identified
form
stable
clusters
with
other
nucleation
precursors,
study
which,
and
how
many,
intermolecular
interactions
are
required
given
OOM
clusters.
In
this
manner
can
reverse
engineer
elusive
structure
candidates
that
might
be
involved
enhanced
We
calculated
binding
free
energies
all
combinations
donor
acceptor
functional
groups
investigate
which
most
preferentially
bind
each
precursors
such
as
sulfuric
acid
bases
(ammonia,
methyl-,
dimethyl-
trimethylamine).
find
multiple
carboxyl
lead
substantially
more
compared
groups.
Employing
dynamics
simulations,
hypothetically
composed
stabilize
acid–base
provide
recommendations
potential
multi-carboxylic
tracer
should
explicitly
studied
future.
presented
generally
applicable
employed
many
applications,
ion-induced
elucidating
structural
patterns
facilitate
ice
nucleation.
Aerosol Research,
Journal Year:
2025,
Volume and Issue:
3(1), P. 125 - 137
Published: Feb. 28, 2025
Abstract.
Sulfuric
acid,
ammonia,
and
amines
are
believed
to
be
key
contributors
the
initial
steps
in
new
particle
formation
atmosphere.
However,
other
compounds
such
as
organic
or
nitric
acid
important
for
further
growth
at
larger
sizes.
In
this
study,
we
investigate
potential
uptake
of
first-generation
oxidation
products
from
α-pinene
(pinic
pinonic
acid)
isoprene
(trans-β-IEPOX,
β4-ISPOOH,
β1-ISOPOOH),
a
highly
oxidised
molecule
(HOM),
formic
acid.
The
is
probed
onto
(SA)10(base)10
freshly
nucleated
particles
(FNPs),
where
SA
denotes
sulfuric
bases
ammonia
(AM),
methylamine
(MA),
dimethylamine
(DMA),
trimethylamine
(TMA).
addition
free
energies
were
calculated
ωB97X-D3BJ/6-311++G(3df,3pd)//B97-3c
level
theory.
We
find
favourable
−8
−10
kcal
mol−1
HOM,
pinic
on
less
sterically
hindered
(SA)10(AM)10
(SA)10(MA)10
FNPs.
This
suggests
that
do
not
contribute
early
FNPs,
but
do,
accordance
with
their
expected
volatilities.
Calculating
second
maintains
its
large
energy
decrease
due
two
carboxylic
groups
interacting
monomer,
well
FNP.
pinonic-acid
drops
−3.9
weak
interactions
between
FNP
carbonyl
group
lack
monomer–monomer
interactions.
under
realistic
atmospheric
conditions,
FNPs
studied
too
small
(1.4
nm)
support
monomers.
accretion
product
pinyl
diaterpenylic
ester
(PDPE;
C17H26O8)
yields
an
value
−17.1
mol−1.
PDPE
can
overcome
strong
Kelvin
effect
1.4
nm
lead
spontaneous
ambient
conditions.
Geoscientific model development,
Journal Year:
2025,
Volume and Issue:
18(9), P. 2701 - 2724
Published: May 15, 2025
Abstract.
The
formation
of
aerosol
particles
in
the
atmosphere
impacts
air
quality
and
climate
change,
but
many
organic
molecules
involved
remain
unknown.
Machine
learning
could
aid
identifying
these
compounds
through
accelerated
analysis
molecular
properties
detection
characteristics.
However,
such
progress
is
hindered
by
current
lack
curated
datasets
for
atmospheric
their
associated
properties.
To
tackle
this
challenge,
we
propose
a
similarity
that
connects
to
existing
large
used
machine
development.
We
find
small
overlap
between
non-atmospheric
using
standard
representations
applications.
identified
out-of-domain
character
related
distinct
functional
groups
atomic
composition.
Our
investigation
underscores
need
collaborative
efforts
gather
share
more
molecular-level
chemistry
data.
presented
similarity-based
can
be
future
dataset
curation
development
sciences.