Organic
solar
cells
(OSCs),
renowned
for
their
lightweight,
cost
efficiency,
and
adaptability
nature,
stand
out
as
a
promising
option
developing
renewable
energy.
Improving
the
power
conversion
efficiency
(PCE)
of
OSCs
is
essential,
researchers
are
delving
into
novel
materials
to
achieve
this.
Traditional
approaches
often
laborious
costly,
highlighting
need
predictive
modeling.
Machine
learning
(ML),
especially
via
quantitative
structure–property
relationship
(QSPR)
models,
streamlining
material
development,
with
goal
exceed
20%
PCE.
In
this
review,
application
ML
in
explored,
recent
studies
utilizing
PCE
prediction
reviewed,
encompassing
empirical
functions,
algorithms,
self‐devised
frameworks,
combination
automated
experimental
technologies.
First,
benefits
predicting
addressed.
Second,
development
high‐efficiency
models
both
fullerene
nonfullerene
acceptors
delved
into.
The
impact
various
algorithm
on
then
assessed,
taking
account
construction
models.
Moreover,
quality
databases
selection
descriptors
considered.
Databases
based
further
categorized.
Finally,
prospects
future
proposed.
Journal of Materials Research and Technology,
Год журнала:
2023,
Номер
27, С. 6729 - 6743
Опубликована: Ноя. 1, 2023
To
explore
high
temperature
ductility,
a
new
Mg-2.70Zn-1.34Y-0.37Ca-0.02Zr
(wt.%)
alloy
has
been
fabricated
by
novel
multidirectional
forging
(MDF)
and
hot
rolling.
The
microstructure
mechanical
properties
were
investigated.
average
grain
size
of
MDF
+
rolled
is
10.89
±
0.90
μm
refined
from
the
as-cast
60.03
0.72
μm.
ultimate
tensile
strength
260.51
1.03
MPa,
yield
192.29
1.21
elongation
17.83
%
obtained
at
room
temperature.
For
behavior,
microstructural
examination
revealed
that
continuous
dynamic
recrystallization
discontinuous
are
main
softening
mechanism
in
range
573–673
K,
while
growth
with
bimodal
grains
twins
discovered
723
K
this
alloy.
X-ray
diffraction
scanning
electron
microscopy
–energy
dispersive
spectroscopy
examinations
constituent
phases
composed
α-Mg
solid
solution
intermetallic
compounds
Ca2Mg6Zn3,
Mg3YZn6
(I-phase),
Mg3Zn3Y2
(W-phase).
evolution,
such
as
desired
temperatures,
related
to
thermal
stability
phases.
failure
215.4
was
demonstrated
673
1.67
×
10−2
s−1,
exhibiting
strain
rate
quasi-superplasticity.
A
power-law
constitutive
equation
established.
deformation
activation
energy
177.948
kJ/mol
stress
exponent
4.494
dominant
elevated
temperatures
573–723
dislocation
climb
controlled
lattice
diffusion.
The
molecular
structures
and
properties
of
donor
acceptor
materials
for
organic
solar
cells
(OSCs)
determine
their
photovoltaic
performance;
however,
the
complex
relationship
between
them
has
hindered
development
OSC
materials.
To
study
this,
we
constructed
database
comprising
544
non‐fullerene
pairs.
Based
on
principle
minimal
rings
units,
each
molecule
in
is
cut
into
different
fragments
defined
as
a
new
fingerprint,
where
bit
corresponds
to
fragment
number
molecule.
Accordingly,
fingerprint
length
234
723
bits
donors
acceptors,
respectively.
Random
forest
extreme
tree
regression
(ETR)
are
applied
predict
parameters,
with
ETR
being
most
effective.
Through
SHapley
Additive
exPlanations
(SHAP)
importance
analysis,
eight
(10)
important
(acceptor)
identified.
Furthermore,
by
computing
similarities
that
obtained
from
SHAP
similarity
exceeding
0.6
collected
order
design
molecules.
By
assembling
fragments,
designed
21
168
D‐
π
‐A‐
‐type
1
156
400
A‐
‐D‐
‐A‐type
nonfullerene
generating
24
478
675
200
donor–acceptor
predictions
using
trained
model,
highest
power
conversion
efficiency
reaches
13.2%.
Molecular Systems Design & Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
Transfer
learning
followed
by
density
functional
theory
accelerates
material
discovery
of
conjugated
oligomers
for
high-efficiency
organic
photovoltaic
materials.
