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.
Journal of Educational Management and Learning,
Journal Year:
2023,
Volume and Issue:
1(1), P. 8 - 15
Published: July 24, 2023
Artificial
intelligence
(AI)
has
emerged
as
a
powerful
technology
that
the
potential
to
transform
education.
This
study
aims
comprehensively
understand
students'
perspectives
on
using
AI
within
educational
settings
gain
insights
about
role
of
in
education
and
investigate
their
perceptions
regarding
advantages,
challenges,
expectations
associated
with
integrating
into
learning
process.
We
analyzed
student
responses
from
survey
targeted
students
diverse
academic
backgrounds
levels.
The
results
show
that,
general,
have
positive
perception
believe
is
beneficial
for
However,
they
are
still
concerned
some
drawbacks
AI.
Therefore,
it
necessary
take
steps
minimize
negative
impact
while
continuing
advantage
advantages
Chaos An Interdisciplinary Journal of Nonlinear Science,
Journal Year:
2023,
Volume and Issue:
33(7)
Published: July 1, 2023
Adaptivity
is
a
dynamical
feature
that
omnipresent
in
nature,
socio-economics,
and
technology.
For
example,
adaptive
couplings
appear
various
real-world
systems,
such
as
the
power
grid,
social,
neural
networks,
they
form
backbone
of
closed-loop
control
strategies
machine
learning
algorithms.
In
this
article,
we
provide
an
interdisciplinary
perspective
on
systems.
We
reflect
notion
terminology
adaptivity
different
disciplines
discuss
which
role
plays
for
fields.
highlight
common
open
challenges
give
perspectives
future
research
directions,
looking
to
inspire
approaches.
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.
