Progress of machine learning in materials design for Li-Ion battery
Prasshanth C.V.,
Arun Kumar Lakshminarayanan,
R. Brindha
и другие.
Next Materials,
Год журнала:
2024,
Номер
2, С. 100145 - 100145
Опубликована: Янв. 1, 2024
The
widespread
adoption
of
lithium-ion
batteries
has
ushered
in
a
transformative
era
across
industries,
powering
an
array
devices
from
portable
electronics
to
electric
vehicles.
This
review
explores
recent
advancements
machine
learning
tools
tailored
for
improving
battery
materials,
management
strategies,
and
system-level
optimization.
It
provides
comprehensive
overview
the
current
landscape,
emphasizing
less-explored
evolution
algorithms
materials.
Machine
integration
enhances
our
understanding
material
properties,
accelerates
discovery
efficient
compositions,
contributes
development
more
durable
batteries.
article
also
delves
into
learnings
role
predicting
State
Health
remaining
useful
life,
crucial
proactive
maintenance.
highlights
how
integrating
field
potential
revolutionize
design
accelerate
energy
storage
technology,
promising
sustainable
technologically
advanced
future.
Язык: Английский
Future of battery thermal management systems (BTMS): Role of advanced technologies, artificial intelligence and sustainability
Next Sustainability,
Год журнала:
2025,
Номер
6, С. 100114 - 100114
Опубликована: Янв. 1, 2025
Язык: Английский
Layered double hydroxides: next promising materials for energy storage and conversion
Next Materials,
Год журнала:
2023,
Номер
1(4), С. 100040 - 100040
Опубликована: Сен. 27, 2023
Layered
double
hydroxides
(LDHs)
are
a
family
of
two-dimensional
(2D)
layered
materials
with
controllable
supramolecular
structure
and
unique
physicochemical
properties,
making
them
highly
attractive
in
the
fields
energy
storage
conversion.
Considering
intense
interest
LDHs
family,
this
review
aims
to
provide
comprehensive
summary
their
development
history,
synthesis
strategies,
energy-related
applications.
Special
attention
is
given
distinctive
properties
LDHs,
such
as
oriented
assembly
topological
transformation,
which
can
serve
systematic
guidance
for
preparation
LDHs-based
nanostructures.
Furthermore,
outlines
both
classical
cutting-edge
applications
electrocatalysis.
Of
particular
interest,
emerging
coupling
system
based
on
electrocatalytic
water
splitting
thoroughly
analyzed.
Finally,
prospects
potential
challenges
discussed,
aiming
raise
awareness
among
researchers
stimulate
further
progress
material
development.
Язык: Английский
Compilation and deciphering MoS2’s physical properties: Accurate benchmark DFT simulations and assessment of advanced quantum methods
Chemical Physics,
Год журнала:
2024,
Номер
580, С. 112229 - 112229
Опубликована: Фев. 8, 2024
Язык: Английский
The Contribution of Artificial Intelligence to Phase Change Materials in Thermal Energy Storage: From Prediction to Optimization
Renewable Energy,
Год журнала:
2024,
Номер
unknown, С. 121973 - 121973
Опубликована: Ноя. 1, 2024
Язык: Английский
Enhancing energy materials with data-driven methods: A roadmap to long-term hydrogen energy sustainability using machine learning
International Journal of Hydrogen Energy,
Год журнала:
2025,
Номер
119, С. 108 - 125
Опубликована: Март 21, 2025
Язык: Английский
Emergent fullerene nanocomposites with conjugated matrices—An overview
Next Materials,
Год журнала:
2024,
Номер
2, С. 100131 - 100131
Опубликована: Янв. 1, 2024
Fullerene,
an
intimate
zero
dimensional
nanocarbon,
has
been
frequently
adopted
as
nanocomposite
reinforcement.
Imperative
utilizations
of
fullerene
and
derived
nanocomposites
have
observed
in
energy,
optical,
electronics
devices
sectors.
Conjugated
or
conductive
polymers
π-conjugation
backbone
system
leading
to
semiconducting
features.
In
addition
fine
electron
conduction,
these
advantages
low
weight,
facile
processing,
chemical
thermal
robustness.
Including
nanocarbon
dopants
conjugated
enhanced
several
technical
features
matrices.
Accordingly,
this
progressive
overview
highlights
design,
properties,
potential
reinforced
conducting
polymer
nanocomposites.
The
structural
multiplicity
polymer/fullerene
enabled
advanced
potential.
Consequently,
ensuing
revealed
efficient
structural,
morphological,
physical
characteristics,
along
with
revolts
technological
fields
like
photovoltaics,
supercapacitors,
sensors,
etc.
Forthcoming
research
on
ground-breaking
may
daze
design/performance
challenges
towards
large
scale
practical
applications.
Язык: Английский
Inorganic–organic modular silicon and dye-sensitized solar cells and predicted role of artificial intelligence towards efficient and stable solar chargers based on supercapacitors
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Март 13, 2024
Abstract
Appropriate
and
rational
management
of
the
energy
produced
by
renewable
sources
is
one
most
urgent
challenges
for
global
sector.
This
paper
devoted
to
systematic
experimental
theoretical
studies
a
modular
solar
charger
based
on
silicon
dye-sensitized
cells
as
an
source,
supercapacitor
bank.
Using
MathCAD
program,
I–V
characteristics
were
plotted
both
single
cell
photovoltaic
module
various
series-to-parallel
connections.
To
assess
surface
quality
modules,
additional
tests
using
thermal
imaging
camera
carried
out
well.
The
charging
(two
series-connected
with
capacity
300
F),
determined
depending
parameters
well
considering
influence
voltage
balancing
system
control
system.
charge,
discharge,
recharge
carefully
analyzed
optimize
operating
conditions,
i.e.
number
cells.
evaluate
stability
operation
time,
their
temperature
dependence
(17–65
°C),
modules
tested
ten
days
under
Central
European
weather
conditions.
Importantly,
comparative
analysis
chargers
different
configurations
showed
increase
in
electrical
proposed
inorganic–organic
concept
compared
alone
rigid
substrate.
Finally,
preliminary
assumptions
(requirements)
developed
regarding
optical
new
that
could
be
used
innovative
instead
along
predicted
role
artificial
intelligence
(AI)
these
devices.
Язык: Английский
Estimating best nanomaterial for energy harvesting through reinforcement learning DQN coupled with fuzzy PROMETHEE under road-based conditions
Sekar Kidambi Raju,
Ganesh Karthikeyan Varadarajan,
Amal H. Alharbi
и другие.
Scientific Reports,
Год журнала:
2024,
Номер
14(1)
Опубликована: Окт. 14, 2024
Energy
harvesters
based
on
nanomaterials
are
getting
more
and
popular,
but
their
way
to
commercial
availability,
some
crucial
issues
still
need
be
solved.
The
objective
of
the
study
is
select
an
appropriate
nanomaterial.
Using
features
Reinforcement
Deep
Q-Network
(DQN)
in
conjunction
with
Fuzzy
PROMETHEE,
proposed
model,
we
present
this
work
a
hybrid
fuzzy
approach
selecting
materials
for
vehicle-environmental-hazardous
substance
(EHS)
combination
that
operates
roadways
under
traffic
conditions.
DQN
able
accumulate
useful
experience
operating
dynamic
environment,
accordingly
deliver
highest
energy
output
at
same
time
bring
consideration
factors
such
as
durability,
cost,
environmental
impact.
PROMETHEE
allows
participation
human
experts
during
decision-making
process,
going
beyond
quantitative
data
typically
learned
by
through
inclusion
qualitative
preferences.
Instead,
method
unites
strength
individual
approaches,
result
providing
highly
resistant
adjustable
material
selection
real
EHS.
pointed
out
can
give
high
efficiency
reference
years
service,
price,
effects.
model
provides
95%
accuracy
computational
300
s,
application
hypothesis
practical
testing
chosen
showed
selected
harvest
fluctuating
conditions
proved
concept
True
Vehicle
Environmental
High-risk
Substance
scenarios.
Язык: Английский
Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning
Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 12, 2024
Predicting
the
properties
for
unseen
materials
exclusively
on
basis
of
chemical
formula
before
synthesis
and
characterization
has
advantages
research
resource
planning.
This
can
be
achieved
using
suitable
structure-free
encoding
machine
learning
methods,
but
additional
processing
decisions
are
required.
In
this
study,
we
compare
a
variety
encodings
algorithms
to
predict
structure/property
relationships
battery
materials.
It
was
found
that
physical
units
used
measure
property
labels
have
an
important
impact
predictive
ability
models,
regardless
computational
approach.
Property
with
respect
weight
give
excellent
performance,
volume
cannot
predicted
confidence
only
information,
even
when
underlying
characteristics
same.
These
results
contrast
previous
studies
unsupervised
classification,
where
excelled,
highlight
how
structural
features
or
represented
plays
role
in
models.
Язык: Английский