Advanced Materials,
Journal Year:
2024,
Volume and Issue:
36(30)
Published: May 25, 2024
Abstract
Computational
chemistry
is
an
indispensable
tool
for
understanding
molecules
and
predicting
chemical
properties.
However,
traditional
computational
methods
face
significant
challenges
due
to
the
difficulty
of
solving
Schrödinger
equations
increasing
cost
with
size
molecular
system.
In
response,
there
has
been
a
surge
interest
in
leveraging
artificial
intelligence
(AI)
machine
learning
(ML)
techniques
silico
experiments.
Integrating
AI
ML
into
increases
scalability
speed
exploration
space.
remain,
particularly
regarding
reproducibility
transferability
models.
This
review
highlights
evolution
from,
complementing,
or
replacing
energy
property
predictions.
Starting
from
models
trained
entirely
on
numerical
data,
journey
set
forth
toward
ideal
model
incorporating
physical
laws
quantum
mechanics.
paper
also
reviews
existing
their
intertwining,
outlines
roadmap
future
research,
identifies
areas
improvement
innovation.
Ultimately,
goal
develop
architectures
capable
accurate
transferable
solutions
equation,
thereby
revolutionizing
experiments
within
materials
science.
Communications Materials,
Journal Year:
2022,
Volume and Issue:
3(1)
Published: Nov. 26, 2022
Abstract
Machine
learning
plays
an
increasingly
important
role
in
many
areas
of
chemistry
and
materials
science,
being
used
to
predict
properties,
accelerate
simulations,
design
new
structures,
synthesis
routes
materials.
Graph
neural
networks
(GNNs)
are
one
the
fastest
growing
classes
machine
models.
They
particular
relevance
for
as
they
directly
work
on
a
graph
or
structural
representation
molecules
therefore
have
full
access
all
relevant
information
required
characterize
In
this
Review,
we
provide
overview
basic
principles
GNNs,
widely
datasets,
state-of-the-art
architectures,
followed
by
discussion
wide
range
recent
applications
GNNs
concluding
with
road-map
further
development
application
GNNs.
Education Sciences,
Journal Year:
2023,
Volume and Issue:
13(7), P. 632 - 632
Published: June 21, 2023
Hybrid
learning
is
a
complex
combination
of
face-to-face
and
online
learning.
This
model
combines
the
use
multimedia
materials
with
traditional
classroom
work.
Virtual
hybrid
employed
alongside
methods.
That
aims
to
investigate
using
Artificial
Intelligence
(AI)
increase
student
engagement
in
settings.
Educators
are
confronted
contemporary
issues
maintaining
their
students’
interest
motivation
as
popularity
education
continues
grow,
where
many
educational
institutions
adopting
this
due
its
flexibility,
student-teacher
engagement,
peer-to-peer
interaction.
AI
will
help
students
communicate,
collaborate,
receive
real-time
feedback,
all
which
challenges
education.
article
examines
advantages
disadvantages
optimal
approaches
for
incorporating
The
research
findings
suggest
that
can
revolutionize
education,
it
enhances
both
instructor
autonomy
while
fostering
more
engaging
interactive
environment.
Science,
Journal Year:
2023,
Volume and Issue:
381(6654), P. 170 - 175
Published: July 13, 2023
Density
functional
theory
(DFT)
plays
a
pivotal
role
for
the
chemical
and
materials
science
due
to
its
relatively
high
predictive
power,
applicability,
versatility
computational
efficiency.
We
review
recent
progress
in
machine
learning
model
developments
which
has
relied
heavily
on
density
synthetic
data
generation
design
of
architectures.
The
general
relevance
these
is
placed
some
broader
context
sciences.
Resulting
DFT
based
models
with
efficiency,
accuracy,
scalability,
transferability
(EAST),
indicates
probable
ways
routine
use
successful
experimental
planning
software
within
self-driving
laboratories.
Physical review. A/Physical review, A,
Journal Year:
2023,
Volume and Issue:
107(1)
Published: Jan. 3, 2023
In
this
Perspective,
the
authors
review
how
machine
learning,
and
more
broadly
methods
of
artificial
intelligence,
are
utilized
in
advancing
quantum
technologies,
specifically
design,
control,
calibration
optimization
devices.
They
also
discuss
open
challenges
field
potential
future
directions
within
next
decade.
npj Computational Materials,
Journal Year:
2023,
Volume and Issue:
9(1)
Published: April 7, 2023
Abstract
Recent
advances
in
machine
learning
(ML)
have
led
to
substantial
performance
improvement
material
database
benchmarks,
but
an
excellent
benchmark
score
may
not
imply
good
generalization
performance.
Here
we
show
that
ML
models
trained
on
Materials
Project
2018
can
severely
degraded
new
compounds
2021
due
the
distribution
shift.
We
discuss
how
foresee
issue
with
a
few
simple
tools.
Firstly,
uniform
manifold
approximation
and
projection
(UMAP)
be
used
investigate
relation
between
training
test
data
within
feature
space.
Secondly,
disagreement
multiple
illuminate
out-of-distribution
samples.
demonstrate
UMAP-guided
query
by
committee
acquisition
strategies
greatly
improve
prediction
accuracy
adding
only
1%
of
data.
believe
this
work
provides
valuable
insights
for
building
databases
enable
better
robustness
generalizability.
Chemistry of Materials,
Journal Year:
2023,
Volume and Issue:
35(8), P. 3046 - 3056
Published: March 9, 2023
Owing
to
the
chemical
pluripotency
and
viscoelastic
nature
of
electronic
polymers,
polymer
electronics
have
shown
unique
advances
in
many
emerging
applications
such
as
skin-like
electronics,
large-area
printed
energy
devices,
neuromorphic
computing
but
their
development
period
is
years-long.
Recent
advancements
automation,
robotics,
learning
algorithms
led
a
growing
number
self-driving
(autonomous)
laboratories
that
begun
revolutionize
accelerated
discovery
materials.
In
this
perspective,
we
first
introduce
current
state
autonomous
laboratories.
Then
analyze
why
it
challenging
conduct
research
by
an
laboratory
highlight
needs.
We
further
discuss
our
efforts
building
laboratory,
namely
Polybot,
for
automated
synthesis
characterization
polymers
processing
fabrication
into
devices.
Finally,
share
vision
using
different
types
research.
