arXiv (Cornell University),
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
The
vast
amount
of
computational
studies
on
electrical
conduction
in
solid-state
electrolytes
is
not
mirrored
by
comparable
efforts
addressing
thermal
conduction,
which
has
been
scarcely
investigated
despite
its
relevance
to
management
and
(over)heating
batteries.
reason
for
this
lies
the
complexity
calculations:
one
hand,
diffusion
ionic
charge
carriers
makes
lattice
methods
formally
unsuitable,
due
lack
equilibrium
atomic
positions
needed
normal-mode
expansion.
On
other
prohibitive
cost
large-scale
molecular
dynamics
(MD)
simulations
heat
transport
large
systems
at
ab
initio
levels
hindered
use
MD-based
methods.
In
paper,
we
leverage
recently
developed
machine-learning
potentials
targeting
different
functionals
(PBEsol,
r$^2$SCAN,
PBE0)
a
state-of-the-art
formulation
Green-Kubo
theory
multicomponent
compute
conductivity
promising
electrolyte,
Li$_3$PS$_4$,
all
polymorphs
($\alpha$,
$\beta$,
$\gamma$).
By
comparing
MD
estimates
with
low-temperature,
nondiffusive
$\gamma$-Li$_3$PS$_4$,
highlight
strong
anharmonicities
negligible
nuclear
quantum
effects,
hence
further
justifying
even
phases.
Finally,
ion-conducting
$\alpha$
$\beta$
phases,
where
approach
mandatory,
our
indicate
weak
temperature
dependence
conductivity,
glass-like
behavior
effective
local
disorder
characterizing
these
Li-diffusing
Chemistry of Materials,
Год журнала:
2024,
Номер
36(3), С. 1482 - 1496
Опубликована: Фев. 5, 2024
Lithium
ortho-thiophosphate
(Li3PS4)
has
emerged
as
a
promising
candidate
for
solid-state
electrolyte
batteries,
thanks
to
its
highly
conductive
phases,
cheap
components,
and
large
electrochemical
stability
range.
Nonetheless,
the
microscopic
mechanisms
of
Li-ion
transport
in
Li3PS4
are
far
from
being
fully
understood,
role
PS4
dynamics
charge
still
controversial.
In
this
work,
we
build
machine
learning
potentials
targeting
state-of-the-art
DFT
references
(PBEsol,
r2SCAN,
PBE0)
tackle
problem
all
known
phases
(α,
β,
γ),
system
sizes
time
scales.
We
discuss
physical
origin
observed
superionic
behavior
Li3PS4:
activation
flipping
drives
structural
transition
phase,
characterized
by
an
increase
Li-site
availability
drastic
reduction
energy
diffusion.
also
rule
out
any
paddle-wheel
effects
tetrahedra
phases─previously
claimed
enhance
diffusion─due
orders-of-magnitude
difference
between
rate
flips
hops
at
temperatures
below
melting.
finally
elucidate
interionic
dynamical
correlations
transport,
highlighting
failure
Nernst–Einstein
approximation
estimate
electrical
conductivity.
Our
results
show
strong
dependence
on
target
reference,
with
PBE0
yielding
best
quantitative
agreement
experimental
measurements
not
only
electronic
band
gap
but
conductivity
β-
α-Li3PS4.
The Journal of Physical Chemistry Letters,
Год журнала:
2024,
Номер
15(30), С. 7539 - 7547
Опубликована: Июль 18, 2024
Ionic
liquids
(ILs)
are
an
exciting
class
of
electrolytes
finding
applications
in
many
areas
from
energy
storage
to
solvents,
where
they
have
been
touted
as
"designer
solvents"
can
be
mixed
precisely
tailor
the
physiochemical
properties.
As
using
machine
learning
interatomic
potentials
(MLIPs)
simulate
ILs
is
still
relatively
unexplored,
several
questions
need
answered
see
if
MLIPs
transformative
for
ILs.
Since
often
not
pure,
but
either
together
or
contain
additives,
we
first
demonstrate
that
a
MLIP
trained
compositionally
transferable;
i.e.,
applied
mixtures
ions
directly
on,
while
only
being
on
few
same
ions.
We
also
investigated
accuracy
novel
IL,
which
experimentally
synthesize
and
characterize.
Our
∼200
DFT
frames
reasonable
agreement
with
our
experiments
DFT.
Frontiers in Materials,
Год журнала:
2024,
Номер
11
Опубликована: Март 6, 2024
Accessing
the
thermal
transport
properties
of
glasses
is
a
major
issue
for
design
production
strategies
glass
industry,
as
well
plethora
applications
and
devices
where
are
employed.
From
computational
standpoint,
chemical
morphological
complexity
calls
atomistic
simulations
interatomic
potentials
able
to
capture
variety
local
environments,
composition,
(dis)order
that
typically
characterize
glassy
phases.
Machine-learning
(MLPs)
emerging
valid
alternative
computationally
expensive
ab
initio
simulations,
inevitably
run
on
very
small
samples
which
cannot
account
disorder
at
different
scales,
empirical
force
fields,
fast
but
often
reliable
only
in
narrow
portion
thermodynamic
composition
phase
diagrams.
In
this
article,
we
make
point
use
MLPs
compute
conductivity
glasses,
through
review
recent
theoretical
tools
series
numerical
vitreous
silica
silicon,
both
pure
intercalated
with
lithium.
Machine Learning Science and Technology,
Год журнала:
2024,
Номер
5(3), С. 035006 - 035006
Опубликована: Июнь 17, 2024
Abstract
Statistical
learning
algorithms
provide
a
generally-applicable
framework
to
sidestep
time-consuming
experiments,
or
accurate
physics-based
modeling,
but
they
introduce
further
source
of
error
on
top
the
intrinsic
limitations
experimental
theoretical
setup.
Uncertainty
estimation
is
essential
quantify
this
error,
and
make
application
data-centric
approaches
more
trustworthy.
To
ensure
that
uncertainty
quantification
used
widely,
one
should
aim
for
are
accurate,
also
easy
implement
apply.
In
particular,
including
an
existing
architecture
be
straightforward,
add
minimal
computational
overhead.
Furthermore,
it
manipulate
combine
multiple
machine-learning
predictions,
propagating
over
modeling
steps.
We
compare
several
well-established
frameworks
against
these
requirements,
propose
practical
approach,
which
we
dub
direct
propagation
shallow
ensembles,
provides
good
compromise
between
ease
use
accuracy.
present
benchmarks
generic
datasets,
in-depth
study
applications
field
atomistic
machine
chemistry
materials.
These
examples
underscore
importance
using
formulation
allows
errors
without
making
strong
assumptions
correlations
different
predictions
model.
Journal of Chemical Theory and Computation,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 10, 2025
While
machine
learning
(ML)
models
have
been
able
to
achieve
unprecedented
accuracies
across
various
prediction
tasks
in
quantum
chemistry,
it
is
now
apparent
that
accuracy
on
a
test
set
alone
not
guarantee
for
robust
chemical
modeling
such
as
stable
molecular
dynamics
(MD).
To
go
beyond
accuracy,
we
use
explainable
artificial
intelligence
(XAI)
techniques
develop
general
analysis
framework
atomic
interactions
and
apply
the
SchNet
PaiNN
neural
network
models.
We
compare
these
with
of
fundamental
principles
understand
how
well
learned
underlying
physicochemical
concepts
from
data.
focus
strength
different
species,
predictions
intensive
extensive
properties
are
made,
analyze
decay
many-body
nature
interatomic
distance.
Models
deviate
too
far
known
physical
produce
unstable
MD
trajectories,
even
when
they
very
high
energy
force
accuracy.
also
suggest
further
improvements
ML
architectures
better
account
polynomial
interactions.
Angewandte Chemie International Edition,
Год журнала:
2024,
Номер
63(22)
Опубликована: Март 22, 2024
The
structure
of
amorphous
silicon
(a-Si)
is
widely
thought
as
a
fourfold-connected
random
network,
and
yet
it
defective
atoms,
with
fewer
or
more
than
four
bonds,
that
make
particularly
interesting.
Despite
many
attempts
to
explain
such
"dangling-bond"
"floating-bond"
defects,
respectively,
unified
understanding
still
missing.
Here,
we
use
advanced
computational
chemistry
methods
reveal
the
complex
structural
energetic
landscape
defects
in
a-Si.
We
study
an
ultra-large-scale,
quantum-accurate
model
containing
million
thousands
individual
allowing
reliable
defect-related
statistics
be
obtained.
combine
descriptors
machine-learned
atomic
energies
develop
classification
different
types
results
suggest
revision
established
floating-bond
by
showing
fivefold-bonded
atoms
a-Si
exhibit
wide
range
local
environments-analogous
fivefold
centers
coordination
chemistry.
Furthermore,
shown
(but
not
threefold)
tend
cluster
together.
Our
provides
new
insights
into
one
most
studied
solids,
has
general
implications
for
disordered
materials
beyond
alone.
Nature Communications,
Год журнала:
2025,
Номер
16(1)
Опубликована: Фев. 26, 2025
Abstract
While
there
have
been
many
developments
in
computational
probes
of
both
strongly-correlated
molecular
systems
and
machine-learning
accelerated
dynamics,
remains
a
significant
gap
capabilities
simulating
accurate
non-local
electronic
structure
over
timescales
on
which
atoms
move.
We
develop
an
approach
to
bridge
these
fields
with
practical
interpolation
scheme
for
the
correlated
many-electron
state
through
space
atomic
configurations,
whilst
avoiding
exponential
complexity
underlying
states.
With
small
number
wave
functions
as
training
set,
we
demonstrate
provable
convergence
near-exact
potential
energy
surfaces
subsequent
dynamics
propagation
valid
many-body
function
inference
its
variational
retaining
mean-field
scaling.
This
represents
profoundly
different
paradigm
direct
established
approaches.
combine
this
modern
approaches
systematically
resolve
trajectories
converge
thermodynamic
quantities
high-throughput
several
million
interpolated
explicit
validation
their
accuracy
from
only
few
numerically
exact
quantum
chemical
calculations.
also
highlight
comparison
traditional
machine-learned
potentials
or
surfaces.