Electronic Structure,
Год журнала:
2024,
Номер
6(4), С. 045006 - 045006
Опубликована: Окт. 25, 2024
Abstract
The
recently
proposed
machine
learning-based
physically-constrained
nonlocal
(MPN)
kinetic
energy
density
functional
(KEDF)
can
be
used
for
simple
metals
and
their
alloys
(Sun
Chen
2024
Phys.
Rev.
B
109
115135).
However,
the
MPN
KEDF
does
not
perform
well
semiconductors.
Here
we
propose
a
multi-channel
(CPN)
KEDF,
which
extends
to
semiconductors
by
integrating
information
collected
from
multiple
channels,
with
each
channel
featuring
specific
length
scale
in
real
space.
CPN
is
systematically
tested
on
silicon
binary
We
find
that
design
beneficial
machine-learning-based
models
capturing
characteristics
of
semiconductors,
particularly
handling
covalent
bonds.
In
particular,
5
utilizes
five
demonstrates
excellent
accuracy
across
all
systems.
These
results
offer
new
path
generating
KEDFs
The Journal of Physical Chemistry Letters,
Год журнала:
2024,
Номер
15(26), С. 6771 - 6781
Опубликована: Июнь 24, 2024
In
mixed
quantum-classical
dynamics,
the
quantum
subsystem
can
have
both
wave
function
and
particle-like
descriptions.
However,
they
may
yield
inconsistent
results
for
expectation
value
of
same
physical
quantity.
We
here
propose
a
novel
detailed
complementary
consistency
(DCC)
method
based
on
principle
internal
consistency.
Namely,
along
each
trajectory
tells
particle
how
to
hop,
while
collapse
active
states
in
ensemble.
As
benchmarked
diverse
array
representative
models
with
localized
nonadiabatic
couplings,
DCC
not
only
achieves
fully
consistent
(i.e.,
identical
populations
calculated
functions
states)
but
also
closely
reproduces
exact
results.
Due
high
performance,
our
new
has
great
potential
give
accurate
description
general
dynamics
after
further
development.
The Journal of Chemical Physics,
Год журнала:
2025,
Номер
162(2)
Опубликована: Янв. 8, 2025
This
study
presents
an
efficient
methodology
for
simulating
nonadiabatic
dynamics
of
complex
materials
with
excitonic
effects
by
integrating
machine
learning
(ML)
models
simplified
Tamm–Dancoff
approximation
(sTDA)
calculations.
By
leveraging
ML
models,
we
accurately
predict
ground-state
wavefunctions
using
unconverged
Kohn–Sham
(KS)
Hamiltonians.
These
ML-predicted
KS
Hamiltonians
are
then
employed
sTDA-based
excited-state
calculations
(sTDA/ML).
The
results
demonstrate
that
energies,
time-derivative
couplings,
and
absorption
spectra
from
sTDA/ML
accurate
enough
compared
those
conventional
density
functional
theory
based
sTDA
(sTDA/DFT)
Furthermore,
sTDA/ML-based
molecular
simulations
on
two
different
systems,
namely
chloro-substituted
silicon
quantum
dot
monolayer
black
phosphorus,
achieve
more
than
100
times
speedup
the
linear
response
time-dependent
DFT
simulations.
work
highlights
potential
ML-accelerated
studying
complicated
photoinduced
large
offering
significant
computational
savings
without
compromising
accuracy.
The Journal of Physical Chemistry Letters,
Год журнала:
2024,
Номер
unknown, С. 9601 - 9619
Опубликована: Сен. 13, 2024
The
all-atomic
full-dimensional-level
simulations
of
nonadiabatic
molecular
dynamics
(NAMD)
in
large
realistic
systems
has
received
high
research
interest
recent
years.
However,
such
NAMD
normally
generate
an
enormous
amount
time-dependent
high-dimensional
data,
leading
to
a
significant
challenge
result
analyses.
Based
on
unsupervised
machine
learning
(ML)
methods,
considerable
efforts
were
devoted
developing
novel
and
easy-to-use
analysis
tools
for
the
identification
photoinduced
reaction
channels
comprehensive
understanding
complicated
motions
simulations.
Here,
we
tried
survey
advances
this
field,
particularly
focus
how
use
ML
methods
analyze
trajectory-based
simulation
results.
Our
purpose
is
offer
discussion
several
essential
components
protocol,
including
selection
construction
descriptors,
establishment
analytical
frameworks,
their
advantages
limitations,
persistent
challenges.
Journal of Physics Condensed Matter,
Год журнала:
2024,
Номер
36(38), С. 385901 - 385901
Опубликована: Июнь 12, 2024
In
this
work,
we
report
the
development
and
assessment
of
nonadiabatic
molecular
dynamics
approach
with
electronic
structure
calculations
based
on
linearly
scaling
subsystem
density
functional
method.
The
is
implemented
in
an
open-source
embedded
Quantum
Espresso/Libra
software
specially
designed
for
simulations
extended
systems.
As
proof
applicability
method
to
large
condensed-matter
systems,
examine
nonradiative
relaxation
excess
excitation
energy
pentacene
crystals
simulation
supercells
containing
more
than
600
atoms.
We
find
that
increased
structural
disorder
observed
larger
supercell
models
induces
couplings
states
accelerates
excited
states.
conduct
a
comparative
analysis
several
quantum-classical
trajectory
surface
hopping
schemes,
including
two
new
methods
proposed
work
(revised
decoherence-induced
instantaneous
decoherence
at
frustrated
hops).
Most
tested
schemes
suggest
fast
occurring
timescales
0.7-2.0
ps
range,
but
they
significantly
overestimate
ground
state
recovery
rates.
Only
modified
simplified
decay
mixing
yields
notably
slower
8-14
ps,
inhibited
recovery.
Nature Communications,
Год журнала:
2025,
Номер
16(1)
Опубликована: Фев. 27, 2025
Abstract
Non-adiabatic
molecular
dynamics
(NAMD)
simulations
have
become
an
indispensable
tool
for
investigating
excited-state
in
solids.
In
this
work,
we
propose
a
general
framework,
N
2
AMD
(Neural-Network
Non-Adiabatic
Molecular
Dynamics),
which
employs
E(3)-equivariant
deep
neural
Hamiltonian
to
boost
the
accuracy
and
efficiency
of
NAMD
simulations.
Distinct
from
conventional
machine
learning
methods
that
predict
key
quantities
NAMD,
computes
these
directly
with
Hamiltonian,
ensuring
excellent
accuracy,
efficiency,
consistency.
not
only
achieves
impressive
performing
at
hybrid
functional
level
within
framework
classical
path
approximation
(CPA),
but
also
demonstrates
great
potential
predicting
non-adiabatic
coupling
vectors
suggests
method
go
beyond
CPA.
Furthermore,
generalizability
enables
seamless
integration
advanced
techniques
infrastructures.
Taking
several
extensively
investigated
semiconductors
as
prototypical
system,
successfully
simulate
carrier
recombination
both
pristine
defective
systems
large
scales
where
often
significantly
underestimates
or
even
qualitatively
incorrectly
predicts
lifetimes.
This
offers
reliable
efficient
approach
conducting
accurate
across
various
condensed
materials.
The Journal of Physical Chemistry Letters,
Год журнала:
2025,
Номер
unknown, С. 5561 - 5569
Опубликована: Май 28, 2025
Two-dimensional
electronic
spectroscopy
(2DES)
provides
rich
information
about
how
the
states
of
molecules,
proteins,
and
solid-state
materials
interact
with
each
other
their
surrounding
environment.
Atomistic
molecular
dynamics
simulations
offer
an
appealing
route
to
uncover
nuclear
motions
mediate
energy
relaxation
manifestation
in
spectroscopies
but
are
computationally
expensive.
Here
we
show
that
by
using
equivariant
transformer-based
machine
learning
architecture
trained
only
2500
ground
state
100
excited
structure
calculations,
one
can
construct
accurate
machine-learned
potential
surfaces
for
both
ground-state
surface
excited-state
gap.
We
demonstrate
utility
this
approach
simulating
Nile
blue
ethanol,
where
experimentally
validate
decompose
simulated
2DES
establish
chromophore
solvent
couple
state,
connecting
spectroscopic
signals
origin.
The Journal of Physical Chemistry Letters,
Год журнала:
2024,
Номер
unknown, С. 11944 - 11953
Опубликована: Ноя. 22, 2024
A
new
algorithm
for
the
identification
of
unavoided
(trivial)
crossings
in
nonadiabatic
molecular
dynamics
calculations
is
reported.
The
approach
does
not
require
knowledge
wave
functions
or
function
time
overlaps
and
uses
only
information
on
state
energies
gradients.
In
addition,
a
simple
phase
consistency
correction
time-derivative
couplings
proposed
situations
which
are
available.
performance
two
algorithms
demonstrated
using
several
crossing
models.
approaches
work
best
systems
with
localized
coupling
regions
but
may
have
difficulties
those
extended
coupling.
It
found
that
tracking
alone
sufficient
producing
correct
population
required.
The Journal of Physical Chemistry Letters,
Год журнала:
2024,
Номер
unknown, С. 153 - 162
Опубликована: Дек. 21, 2024
In
this
work,
a
machine
learning
mapping
approach
for
predicting
the
properties
of
atomistic
systems
is
reported.
Within
approach,
atomic
orbital
overlap,
density,
or
Kohn-Sham
(KS)
Fock
matrix
elements
obtained
at
low
level
theory
such
as
extended
tight-binding
have
been
used
input
features
to
predict
electric
field
gradient
(EFG)
tensors
higher
those
with
hybrid
functionals.
It
shown
that
machine-learning-predicted
EFG
can
be
compute
spin
relaxation
rates
several
ions
in
aqueous
solutions.
From
only
fraction
data
direct
calculation,
one
quadrupolar
isotropic
good
accuracy,
achieving
relative
errors
between
about
2–8%
different
ions.
In
mixed
quantum-classical
dynamics,
the
quantum
subsystem
can
have
both
wave
function
and
particle-like
descriptions.
However,
they
may
yield
inconsistent
results
for
expectation
value
of
same
physical
quantity.
We
here
propose
a
novel
detailed
complementary
consistency
(DCC)
method
based
on
principle
internal
consistency.
Namely,
along
each
trajectory
tells
particle
how
to
hop,
while
collapse
active
states
in
ensemble.
As
benchmarked
diverse
array
representative
models,
DCC
not
only
achieves
fully
consistent
(i.e.,
identical
populations
calculated
functions
states),
but
also
closely
reproduces
exact
results.
Due
high
performance,
our
new
is
promising
accurate
description
general
nonadiabatic
dynamics.
In
mixed
quantum-classical
dynamics,
the
quantum
subsystem
can
have
both
wave
function
and
particle-like
descriptions.
However,
they
may
yield
inconsistent
results
for
expectation
value
of
same
physical
quantity.
We
here
propose
a
novel
detailed
complementary
consistency
(DCC)
method
based
on
principle
internal
consistency.
Namely,
along
each
trajectory
tells
particle
how
to
hop,
while
collapse
active
states
in
ensemble.
As
benchmarked
diverse
array
representative
models
with
localized
nonadiabatic
couplings,
DCC
not
only
achieves
fully
consistent
(i.e.,
identical
populations
calculated
functions
states),
but
also
closely
reproduces
exact
results.
Due
high
performance,
our
new
has
great
potential
give
accurate
description
general
dynamics
after
further
development.