Nature Communications,
Год журнала:
2021,
Номер
12(1)
Опубликована: Ноя. 15, 2021
Abstract
Artificial
intelligence
(AI)
and
machine
learning
(ML)
have
been
increasingly
used
in
materials
science
to
build
predictive
models
accelerate
discovery.
For
selected
properties,
availability
of
large
databases
has
also
facilitated
application
deep
(DL)
transfer
(TL).
However,
unavailability
datasets
for
a
majority
properties
prohibits
widespread
DL/TL.
We
present
cross-property
deep-transfer-learning
framework
that
leverages
trained
on
small
different
properties.
test
the
proposed
39
computational
two
experimental
find
TL
with
only
elemental
fractions
as
input
outperform
ML/DL
from
scratch
even
when
they
are
allowed
use
physical
attributes
input,
27/39
(≈
69%)
both
datasets.
believe
can
be
widely
useful
tackle
data
challenge
applying
AI/ML
science.
The Innovation,
Год журнала:
2021,
Номер
2(4), С. 100179 - 100179
Опубликована: Окт. 29, 2021
•"Can
machines
think?"
The
goal
of
artificial
intelligence
(AI)
is
to
enable
mimic
human
thoughts
and
behaviors,
including
learning,
reasoning,
predicting,
so
on.•"Can
AI
do
fundamental
research?"
coupled
with
machine
learning
techniques
impacting
a
wide
range
sciences,
mathematics,
medical
science,
physics,
etc.•"How
does
accelerate
New
research
applications
are
emerging
rapidly
the
support
by
infrastructure,
data
storage,
computing
power,
algorithms,
frameworks.
Artificial
promising
(ML)
well
known
from
computer
science
broadly
affecting
many
aspects
various
fields
technology,
industry,
even
our
day-to-day
life.
ML
have
been
developed
analyze
high-throughput
view
obtaining
useful
insights,
categorizing,
making
evidence-based
decisions
in
novel
ways,
which
will
promote
growth
fuel
sustainable
booming
AI.
This
paper
undertakes
comprehensive
survey
on
development
application
different
information
materials
geoscience,
life
chemistry.
challenges
that
each
discipline
meets,
potentials
handle
these
challenges,
discussed
detail.
Moreover,
we
shed
light
new
trends
entailing
integration
into
scientific
discipline.
aim
this
provide
broad
guideline
sciences
potential
infusion
AI,
help
motivate
researchers
deeply
understand
state-of-the-art
AI-based
thereby
continuous
sciences.
Chemical Society Reviews,
Год журнала:
2021,
Номер
50(22), С. 12450 - 12550
Опубликована: Янв. 1, 2021
Dye-sensitized
solar
cells
(DSCs)
are
celebrating
their
30th
birthday
and
they
attracting
a
wealth
of
research
efforts
aimed
at
unleashing
full
potential.
In
recent
years,
DSCs
dye-sensitized
photoelectrochemical
(DSPECs)
have
experienced
renaissance
as
the
best
technology
for
several
niche
applications
that
take
advantage
DSCs'
unique
combination
properties:
low
cost,
composed
non-toxic
materials,
colorful,
transparent,
very
efficient
in
light
conditions.
This
review
summarizes
advancements
field
over
last
decade,
encompassing
all
aspects
DSC
technology:
theoretical
studies,
characterization
techniques,
drivers
synthesis
fuels,
commercialization
from
various
companies.
Chemical Reviews,
Год журнала:
2021,
Номер
122(12), С. 10899 - 10969
Опубликована: Сен. 16, 2021
This
is
a
critical
review
of
artificial
intelligence/machine
learning
(AI/ML)
methods
applied
to
battery
research.
It
aims
at
providing
comprehensive,
authoritative,
and
critical,
yet
easily
understandable,
general
interest
the
community.
addresses
concepts,
approaches,
tools,
outcomes,
challenges
using
AI/ML
as
an
accelerator
for
design
optimization
next
generation
batteries─a
current
hot
topic.
intends
create
both
accessibility
these
tools
chemistry
electrochemical
energy
sciences
communities
completeness
in
terms
different
R&D
aspects
covered.
Patterns,
Год журнала:
2022,
Номер
3(10), С. 100588 - 100588
Опубликована: Окт. 1, 2022
Artificial
intelligence
(AI)
and
machine
learning
(ML)
are
expanding
in
popularity
for
broad
applications
to
challenging
tasks
chemistry
materials
science.
Examples
include
the
prediction
of
properties,
discovery
new
reaction
pathways,
or
design
molecules.
The
needs
read
write
fluently
a
chemical
language
each
these
tasks.
Strings
common
tool
represent
molecular
graphs,
most
popular
string
representation,
Smiles,
has
powered
cheminformatics
since
late
1980s.
However,
context
AI
ML
chemistry,
Smiles
several
shortcomings—most
pertinently,
combinations
symbols
lead
invalid
results
with
no
valid
interpretation.
To
overcome
this
issue,
molecules
was
introduced
2020
that
guarantees
100%
robustness:
SELF-referencing
embedded
(Selfies).
Selfies
simplified
enabled
numerous
chemistry.
In
perspective,
we
look
future
discuss
representations,
along
their
respective
opportunities
challenges.
We
propose
16
concrete
projects
robust
representations.
These
involve
extension
toward
domains,
exciting
questions
at
interface
languages,
interpretability
both
humans
machines.
hope
proposals
will
inspire
follow-up
works
exploiting
full
potential
representations
Angewandte Chemie International Edition,
Год журнала:
2021,
Номер
60(46), С. 24354 - 24366
Опубликована: Июль 1, 2021
Abstract
Emerging
machine
learning
(ML)
methods
are
widely
applied
in
chemistry
and
materials
science
studies
have
led
to
a
focus
on
data‐driven
research.
This
Minireview
summarizes
the
application
of
ML
rechargeable
batteries,
from
microscale
macroscale.
Specifically,
offers
strategy
explore
new
functionals
for
density
functional
theory
calculations
potentials
molecular
dynamics
simulations,
which
expected
significantly
enhance
challenging
descriptions
interfaces
amorphous
structures.
also
possesses
great
potential
mine
unveil
valuable
information
both
experimental
theoretical
datasets.
A
quantitative
“structure–function”
correlation
can
thus
be
established,
used
predict
ionic
conductivity
solids
as
well
battery
lifespan.
exhibits
advantages
optimization,
such
fast‐charge
procedures.
The
future
combination
multiscale
experiments,
is
discussed
role
humans
research
highlighted.
Journal of the American Chemical Society,
Год журнала:
2021,
Номер
143(42), С. 17677 - 17689
Опубликована: Окт. 12, 2021
Modern
polymer
science
suffers
from
the
curse
of
multidimensionality.
The
large
chemical
space
imposed
by
including
combinations
monomers
into
a
statistical
copolymer
overwhelms
synthesis
and
characterization
technology
limits
ability
to
systematically
study
structure–property
relationships.
To
tackle
this
challenge
in
context
19F
magnetic
resonance
imaging
(MRI)
agents,
we
pursued
computer-guided
materials
discovery
approach
that
combines
synergistic
innovations
automated
flow
machine
learning
(ML)
method
development.
A
software-controlled,
continuous
platform
was
developed
enable
iterative
experimental–computational
cycles
resulted
397
unique
compositions
within
six-variable
compositional
space.
nonintuitive
design
criteria
identified
ML,
which
were
accomplished
exploring
<0.9%
overall
space,
lead
identification
>10
outperformed
state-of-the-art
materials.
Advanced Energy Materials,
Год журнала:
2022,
Номер
12(20)
Опубликована: Март 29, 2022
Abstract
The
solar‐energy‐driven
photoreduction
of
CO
2
has
recently
emerged
as
a
promising
approach
to
directly
transform
into
valuable
energy
sources
under
mild
conditions.
As
clean‐burning
fuel
and
drop‐in
replacement
for
natural
gas,
CH
4
is
an
ideal
product
photoreduction,
but
the
development
highly
active
selective
semiconductor‐based
photocatalysts
this
important
transformation
remains
challenging.
Hence,
significant
efforts
have
been
made
in
search
active,
selective,
stable,
sustainable
photocatalysts.
In
review,
recent
applications
cutting‐edge
experimental
computational
materials
design
strategies
toward
discovery
novel
catalysts
photocatalytic
conversion
are
systematically
summarized.
First,
insights
effective
catalyst
engineering
strategies,
including
heterojunctions,
defect
engineering,
cocatalysts,
surface
modification,
facet
single
atoms,
presented.
Then,
data‐driven
photocatalyst
spanning
density
functional
theory
(DFT)
simulations,
high‐throughput
screening,
machine
learning
(ML)
presented
through
step‐by‐step
introduction.
combination
DFT,
ML,
experiments
emphasized
powerful
solution
accelerating
reduction
.
Last,
challenges
perspectives
concerning
interplay
between
rational
industrialization
large‐scale
technologies
described.
