Energy Reports,
Journal Year:
2022,
Volume and Issue:
8, P. 3424 - 3436
Published: March 4, 2022
Oil
resources
affect
the
development
of
global
economy,
so
forecasting
oil
consumption
is
a
necessary
basis
for
formulating
economic
and
social
plans.
In
this
paper,
characteristics
all
background
values
in
system
are
considered,
genetic
optimization
algorithm
used
to
establish
new
nonlinear
multivariable
Verhulst
model.
This
model
weakens
demand
saturated
S-shaped
single-peaked
data,
thus
increasing
its
applicability.
To
verify
validity
novel
extended
model,
eight
evaluation
indices
utilized
actual
cases.
The
outcomes
reveal
that
proposed
significantly
outperforms
preoptimized
grey
multivariate
other
traditional
models.
Finally,
employed
prediction
China,
comparison
made
with
nine
models,
including
neural
network
ARIMA,
linear
findings
metrics
show
second
only
performance,
gap
being
small.
predicts
China's
will
increase
by
24.6641%
2024.
forecasted
information
can
provide
reference
relevant
units
individuals
China
market.
Advanced Energy Materials,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 10, 2024
Abstract
This
review
highlights
recent
advances
in
machine
learning
(ML)‐assisted
design
of
energy
materials.
Initially,
ML
algorithms
were
successfully
applied
to
screen
materials
databases
by
establishing
complex
relationships
between
atomic
structures
and
their
resulting
properties,
thus
accelerating
the
identification
candidates
with
desirable
properties.
Recently,
development
highly
accurate
interatomic
potentials
generative
models
has
not
only
improved
robust
prediction
physical
but
also
significantly
accelerated
discovery
In
past
couple
years,
methods
have
enabled
high‐precision
first‐principles
predictions
electronic
optical
properties
for
large
systems,
providing
unprecedented
opportunities
science.
Furthermore,
ML‐assisted
microstructure
reconstruction
physics‐informed
solutions
partial
differential
equations
facilitated
understanding
microstructure–property
relationships.
Most
recently,
seamless
integration
various
platforms
led
emergence
autonomous
laboratories
that
combine
quantum
mechanical
calculations,
language
models,
experimental
validations,
fundamentally
transforming
traditional
approach
novel
synthesis.
While
highlighting
aforementioned
advances,
existing
challenges
are
discussed.
Ultimately,
is
expected
fully
integrate
atomic‐scale
simulations,
reverse
engineering,
process
optimization,
device
fabrication,
empowering
system
design.
will
drive
transformative
innovations
conversion,
storage,
harvesting
technologies.
