Physical review. B./Physical review. B,
Год журнала:
2023,
Номер
108(20)
Опубликована: Ноя. 8, 2023
In
this
paper,
we
report
on
the
thermal
conductivity
of
${\mathrm{RbV}}_{3}{\mathrm{Sb}}_{5}$
and
${\mathrm{CsV}}_{3}{\mathrm{Sb}}_{5}$
with
three-dimensional
charge
density
wave
phase
transitions
from
40
to
500
K
measured
by
pump-probe
thermoreflectance
techniques.
At
room
temperature,
in-plane
(basal
plane)
conductivities
are
found
be
moderate,
$12\mathrm{W}\phantom{\rule{0.16em}{0ex}}{\mathrm{m}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$
$8.8\phantom{\rule{0.16em}{0ex}}\mathrm{W}\phantom{\rule{0.16em}{0ex}}{\mathrm{m}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$
${\mathrm{CsV}}_{3}{\mathrm{Sb}}_{5}$,
ultralow
cross-plane
(stacking
direction)
observed,
$0.72\phantom{\rule{0.16em}{0ex}}\mathrm{W}\phantom{\rule{0.16em}{0ex}}{\mathrm{m}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$
$0.49\phantom{\rule{0.16em}{0ex}}\mathrm{W}\phantom{\rule{0.16em}{0ex}}{\mathrm{m}}^{\ensuremath{-}1}\phantom{\rule{0.16em}{0ex}}{\mathrm{K}}^{\ensuremath{-}1}$
${\mathrm{CsV}}_{3}{\mathrm{Sb}}_{5}$.
A
unique
glasslike
temperature
dependence
in
is
which
decreases
monotonically
even
lower
than
Cahill-Pohl
limit
as
below
transition
point
${T}_{\mathrm{CDW}}$.
This
obey
hopping
transport
picture.
addition,
a
peak
observed
at
${T}_{\mathrm{CDW}}$
fingerprint
modulated
structural
distortion
along
stacking
direction.
Nature Communications,
Год журнала:
2024,
Номер
15(1)
Опубликована: Март 25, 2024
Abstract
High-efficient
heat
dissipation
plays
critical
role
for
high-power-density
electronics.
Experimental
synthesis
of
ultrahigh
thermal
conductivity
boron
arsenide
(BAs,
1300
W
m
−1
K
)
cooling
substrates
into
the
wide-bandgap
semiconductor
gallium
nitride
(GaN)
devices
has
been
realized.
However,
lack
systematic
analysis
on
transfer
across
GaN-BAs
interface
hampers
practical
applications.
In
this
study,
by
constructing
accurate
and
high-efficient
machine
learning
interatomic
potentials,
we
perform
multiscale
simulations
heterostructures.
Ultrahigh
interfacial
conductance
260
MW
−2
is
achieved,
which
lies
in
well-matched
lattice
vibrations
BAs
GaN.
The
strong
temperature
dependence
found
between
300
to
450
K.
Moreover,
competition
grain
size
boundary
resistance
revealed
with
increasing
from
1
nm
1000
μm.
Such
deep-potential
equipped
not
only
promote
applications
electronics,
but
also
offer
approach
designing
advanced
management
systems.
Advanced Energy Materials,
Год журнала:
2024,
Номер
14(22)
Опубликована: Март 19, 2024
Abstract
Lithium‐ion
batteries
(LIBs)
have
played
an
essential
role
in
the
energy
storage
industry
and
dominated
power
sources
for
consumer
electronics
electric
vehicles.
Understanding
electrochemistry
of
LIBs
at
molecular
scale
is
significant
improving
their
performance,
stability,
lifetime,
safety.
Classical
dynamics
(MD)
simulations
could
directly
capture
atomic
motions
thus
provide
dynamic
insights
into
electrochemical
processes
ion
transport
during
charging
discharging
that
are
usually
challenging
to
observe
experimentally,
which
momentous
developing
with
superb
performance.
This
review
discusses
developments
MD
approaches
using
non‐reactive
force
fields,
reactive
machine
learning
potential
modeling
chemical
reactions
reactants
electrodes,
electrolytes,
electrode‐electrolyte
interfaces.
It
also
comprehensively
how
interactions,
structures,
transport,
reaction
affect
electrode
capacity,
interfacial
properties.
Finally,
remaining
challenges
envisioned
future
routes
commented
on
high‐fidelity,
effective
simulation
methods
decode
invisible
interactions
LIBs.
The Journal of Physical Chemistry Letters,
Год журнала:
2023,
Номер
14(7), С. 1808 - 1822
Опубликована: Фев. 10, 2023
Thermoelectric
(TE)
materials
can
directly
convert
heat
to
electricity
and
vice
versa
have
broad
application
potential
for
solid-state
power
generation
refrigeration.
Over
the
past
few
decades,
efforts
been
made
develop
new
TE
with
high
performance.
However,
traditional
experiments
simulations
are
expensive
time-consuming,
limiting
development
of
materials.
Machine
learning
(ML)
has
increasingly
applied
study
in
recent
years.
This
paper
reviews
progress
ML-based
material
research.
The
ML
predicting
optimizing
properties
materials,
including
electrical
thermal
transport
optimization
functional
targeted
properties,
is
reviewed.
Finally,
future
research
directions
discussed.
