Macromolecular Symposia,
Journal Year:
2025,
Volume and Issue:
414(1)
Published: Feb. 1, 2025
Abstract
Artificial
intelligence
(AI)
and
machine
learning
(ML)
have
advanced
tremendously
in
the
previous
5
years
regarding
polymer
science.
Polymers
are
materials
with
enormous
versatility
that
now
widely
used.
found
extensive
applications
several
fields
such
as
energy
storage,
construction,
medical,
aerospace,
other
industries.
This
study
is
presently
era
of
4.0
industry,
a
transformative
period
profoundly
reshaping
both
business
society
an
unprecedented
manner
specifically
developing
countries.
Data‐driven
strategies
for
process
analysis
control
crucial
expediting
creation
production
processes
while
maintaining
product
quality
avoiding
rise
cost.
More
more
scientists
utilizing
informatics
data
science
to
create
new
understand
connections
between
their
molecular
structure
characteristics.
The
field
relatively
new.
Even
though
there
lot
helpful
databases
tools
accessible,
not
many
used
frequently.
application
AI
starting
influence
on
aspects
human
existence,
including
technology.
Polymer
utilizes
ML
techniques
enhance
developing,
designing,
discovering
polymers.
Based
these
ideas,
it
examines
burgeoning
ML‐assisted
this
research.
It
also
looks
at
developments
polymeric
ecosystem
talks
about
upcoming
potential
problems
applications.
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: July 11, 2023
Polymers
are
a
vital
part
of
everyday
life.
Their
chemical
universe
is
so
large
that
it
presents
unprecedented
opportunities
as
well
significant
challenges
to
identify
suitable
application-specific
candidates.
We
present
complete
end-to-end
machine-driven
polymer
informatics
pipeline
can
search
this
space
for
candidates
at
speed
and
accuracy.
This
includes
fingerprinting
capability
called
polyBERT
(inspired
by
Natural
Language
Processing
concepts),
multitask
learning
approach
maps
the
fingerprints
host
properties.
linguist
treats
structure
polymers
language.
The
outstrips
best
presently
available
concepts
property
prediction
based
on
handcrafted
fingerprint
schemes
in
two
orders
magnitude
while
preserving
accuracy,
thus
making
strong
candidate
deployment
scalable
architectures
including
cloud
infrastructures.
npj Computational Materials,
Journal Year:
2023,
Volume and Issue:
9(1)
Published: April 22, 2023
Abstract
Accurate
and
efficient
prediction
of
polymer
properties
is
great
significance
in
design.
Conventionally,
expensive
time-consuming
experiments
or
simulations
are
required
to
evaluate
functions.
Recently,
Transformer
models,
equipped
with
self-attention
mechanisms,
have
exhibited
superior
performance
natural
language
processing.
However,
such
methods
not
been
investigated
sciences.
Herein,
we
report
TransPolymer,
a
Transformer-based
model
for
property
prediction.
Our
proposed
tokenizer
chemical
awareness
enables
learning
representations
from
sequences.
Rigorous
on
ten
benchmarks
demonstrate
the
TransPolymer.
Moreover,
show
that
TransPolymer
benefits
pretraining
large
unlabeled
dataset
via
Masked
Language
Modeling.
Experimental
results
further
manifest
important
role
modeling
We
highlight
this
as
promising
computational
tool
promoting
rational
design
understanding
structure-property
relationships
data
science
view.
ACS Polymers Au,
Journal Year:
2023,
Volume and Issue:
3(3), P. 239 - 258
Published: Jan. 18, 2023
In
the
last
five
years,
there
has
been
tremendous
growth
in
machine
learning
and
artificial
intelligence
as
applied
to
polymer
science.
Here,
we
highlight
unique
challenges
presented
by
polymers
how
field
is
addressing
them.
We
focus
on
emerging
trends
with
an
emphasis
topics
that
have
received
less
attention
review
literature.
Finally,
provide
outlook
for
field,
outline
important
areas
science
discuss
advances
from
greater
material
community.
ACS Central Science,
Journal Year:
2023,
Volume and Issue:
9(2), P. 206 - 216
Published: Jan. 23, 2023
Solid
polymer
electrolytes
(SPEs)
have
the
potential
to
improve
lithium-ion
batteries
by
enhancing
safety
and
enabling
higher
energy
densities.
However,
SPEs
suffer
from
significantly
lower
ionic
conductivity
than
liquid
solid
ceramic
electrolytes,
limiting
their
adoption
in
functional
batteries.
To
facilitate
more
rapid
discovery
of
high
SPEs,
we
developed
a
chemistry-informed
machine
learning
model
that
accurately
predicts
SPEs.
The
was
trained
on
SPE
data
hundreds
experimental
publications.
Our
encodes
Arrhenius
equation,
which
describes
temperature
activated
processes,
into
readout
layer
state-of-the-art
message
passing
neural
network
has
improved
accuracy
over
models
do
not
encode
dependence.
Chemically
informed
layers
are
compatible
with
deep
for
other
property
prediction
tasks
especially
useful
where
limited
training
available.
Using
model,
values
were
predicted
several
thousand
candidate
formulations,
allowing
us
identify
promising
We
also
generated
predictions
different
anions
poly(ethylene
oxide)
poly(trimethylene
carbonate),
demonstrating
utility
our
identifying
descriptors
conductivity.
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: Aug. 10, 2023
Abstract
Polymers
are
ubiquitous
to
almost
every
aspect
of
modern
society
and
their
use
in
medical
products
is
similarly
pervasive.
Despite
this,
the
diversity
commercial
polymers
used
medicine
stunningly
low.
Considerable
time
resources
have
been
extended
over
years
towards
development
new
polymeric
biomaterials
which
address
unmet
needs
left
by
current
generation
medical-grade
polymers.
Machine
learning
(ML)
presents
an
unprecedented
opportunity
this
field
bypass
need
for
trial-and-error
synthesis,
thus
reducing
invested
into
discoveries
critical
advancing
treatments.
Current
efforts
pioneering
applied
ML
polymer
design
employed
combinatorial
high
throughput
experimental
data
availability
concerns.
However,
lack
available
standardized
characterization
parameters
relevant
medicine,
including
degradation
biocompatibility,
represents
a
nearly
insurmountable
obstacle
ML-aided
biomaterials.
Herein,
we
identify
gap
at
intersection
biomedical
design,
highlight
works
junction
more
broadly
provide
outlook
on
challenges
future
directions.
ACS Applied Bio Materials,
Journal Year:
2023,
Volume and Issue:
7(2), P. 510 - 527
Published: Jan. 26, 2023
Polymers,
with
the
capacity
to
tunably
alter
properties
and
response
based
on
manipulation
of
their
chemical
characteristics,
are
attractive
components
in
biomaterials.
Nevertheless,
potential
as
functional
materials
is
also
inhibited
by
complexity,
which
complicates
rational
or
brute-force
design
realization.
In
recent
years,
machine
learning
has
emerged
a
useful
tool
for
facilitating
via
efficient
modeling
structure–property
relationships
domain
interest.
this
Spotlight,
we
discuss
emergence
data-driven
polymers
that
can
be
deployed
biomaterials
particular
emphasis
complex
copolymer
systems.
We
outline
developments,
well
our
own
contributions
takeaways,
related
high-throughput
data
generation
polymer
systems,
methods
surrogate
learning,
paradigms
property
optimization
design.
Throughout
discussion,
highlight
key
aspects
successful
strategies
other
considerations
will
relevant
future
polymer-based
target
properties.
SmartMat,
Journal Year:
2025,
Volume and Issue:
6(1)
Published: Jan. 9, 2025
ABSTRACT
Machine
learning
(ML),
material
genome,
and
big
data
approaches
are
highly
overlapped
in
their
strategies,
algorithms,
models.
They
can
target
various
definitions,
distributions,
correlations
of
concerned
physical
parameters
given
polymer
systems,
have
expanding
applications
as
a
new
paradigm
indispensable
to
conventional
ones.
Their
inherent
advantages
building
quantitative
multivariate
largely
enhanced
the
capability
scientific
understanding
discoveries,
thus
facilitating
mechanism
exploration,
prediction,
high‐throughput
screening,
optimization,
rational
inverse
designs.
This
article
summarizes
representative
progress
recent
two
decades
focusing
on
design,
preparation,
application,
sustainable
development
materials
based
exploration
key
composition–process–structure–property–performance
relationship.
The
integration
both
data‐driven
insights
through
ML
deepen
fundamental
discover
novel
is
categorically
presented.
Despite
construction
application
robust
models,
strategies
algorithms
deal
with
variant
tasks
science
still
rapid
growth.
challenges
prospects
then
We
believe
that
innovation
will
thrive
along
approaches,
from
efficient
design
applications.
Advanced Intelligent Systems,
Journal Year:
2023,
Volume and Issue:
5(4)
Published: Jan. 31, 2023
In
the
recent
decades,
with
rapid
development
in
computing
power
and
algorithms,
machine
learning
(ML)
has
exhibited
its
enormous
potential
new
polymer
discovery.
Herein,
history
of
ML
is
described
basic
process
accelerated
discovery
summarized.
Next,
four
steps
this
are
reviewed,
that
is,
dataset
selection,
fingerprinting,
framework,
generation.
Finally,
a
couple
main
challenges
for
presented
outlooks
field
prospected.
It
expected
review
can
service
as
useful
tool
people
who
just
step
into
deepen
understanding
already
field.