Data-driven
research
in
chemistry
has
emerged
as
a
new
platform
to
identify
potential
molecules,
examine
dynamic
reaction
mechanisms,
and
extract
knowledge
from
vast
sets
of
data
that
are
made
possible
by
the
use
rapidly
growing
machine
learning
(ML)
approaches.
The
ML-based
models
can
speed
up
computational
algorithms
enhance
findings
make
chemical
sciences
more
effective.
This
chapter
provides
basic
introduction
collection,
processing,
model
validation
approaches,
basics
common
ML
models,
application
such
catalysis.
Finally,
it
discusses
how
may
be
utilized
provide
relevant
predictions
areas
atomistic
understanding
Advanced Materials,
Journal Year:
2023,
Volume and Issue:
36(6)
Published: Oct. 10, 2023
Abstract
Combining
materials
science,
artificial
intelligence
(AI),
physical
chemistry,
and
other
disciplines,
informatics
is
continuously
accelerating
the
vigorous
development
of
new
materials.
The
emergence
“GPT
(Generative
Pre‐trained
Transformer)
AI”
shows
that
scientific
research
field
has
entered
era
intelligent
civilization
with
“data”
as
basic
factor
“algorithm
+
computing
power”
core
productivity.
continuous
innovation
AI
will
impact
cognitive
laws
methods,
reconstruct
knowledge
wisdom
system.
This
leads
to
think
more
about
informatics.
Here,
a
comprehensive
discussion
models
infrastructures
provided,
advances
in
discovery
design
are
reviewed.
With
rise
paradigms
triggered
by
“AI
for
Science”,
vane
informatics:
“MatGPT”,
proposed
technical
path
planning
from
aspects
data,
descriptors,
generative
models,
pretraining
directed
collaborative
training,
experimental
robots,
well
efforts
preparations
needed
develop
generation
informatics,
carried
out.
Finally,
challenges
constraints
faced
discussed,
order
achieve
digital,
intelligent,
automated
construction
joint
interdisciplinary
scientists.
Applied Physics Reviews,
Journal Year:
2025,
Volume and Issue:
12(1)
Published: Feb. 6, 2025
Electrochemical
reactions
are
pivotal
for
energy
conversion
and
storage
to
achieve
a
carbon-neutral
sustainable
society,
optimal
electrocatalysts
essential
their
industrial
applications.
Theoretical
modeling
methodologies,
such
as
density
functional
theory
(DFT)
molecular
dynamics
(MD),
efficiently
assess
electrochemical
reaction
mechanisms
electrocatalyst
performance
at
atomic
levels.
However,
its
intrinsic
algorithm
limitations
high
computational
costs
large-scale
systems
generate
gaps
between
experimental
observations
calculation
simulation,
restricting
the
accuracy
efficiency
of
design.
Combining
machine
learning
(ML)
is
promising
strategy
accelerate
development
electrocatalysts.
The
ML-DFT
frameworks
establish
accurate
property–structure–performance
relations
predict
verify
novel
electrocatalysts'
properties
performance,
providing
deep
understanding
mechanisms.
ML-based
methods
also
solution
MD
DFT.
Moreover,
integrating
ML
experiment
characterization
techniques
represents
cutting-edge
approach
insights
into
structural,
electronic,
chemical
changes
under
working
conditions.
This
review
will
summarize
DFT
current
application
status
design
in
various
conversions.
underlying
physical
fundaments,
advancements,
challenges
be
summarized.
Finally,
future
research
directions
prospects
proposed
guide
revolution.
ACS ES&T Engineering,
Journal Year:
2023,
Volume and Issue:
4(1), P. 66 - 95
Published: Oct. 12, 2023
The
constant
development
of
computer
systems
and
infrastructure
has
allowed
computational
chemistry
to
become
an
important
component
environmental
research.
In
the
past
decade,
application
quantum
classical
mechanical
calculations
model
understand
increased
exponentially.
this
review,
we
highlight
various
applications
techniques
in
areas
research
(e.g.,
wastewater/air
treatment,
sensing,
biodegradation).
We
briefly
describe
each
approach,
starting
with
principle
methods
followed
by
molecular
mechanics
(MM),
dynamics
(MD),
hybrid
QM/MM
methods.
recent
introduction
artificial
intelligence
machine
learning
their
potential
disrupt
field
are
also
discussed.
Challenges
current
future
directions
address
them
presented.
Journal of Energy Chemistry,
Journal Year:
2023,
Volume and Issue:
90, P. 540 - 564
Published: Nov. 26, 2023
The
global
concerns
of
energy
crisis
and
climate
change,
primarily
caused
by
carbon
dioxide
(CO2),
are
utmost
importance.
Recently,
the
electrocatalytic
CO2
reduction
reaction
(CO2RR)
to
high
value-added
multi-carbon
(C2+)
products
driven
renewable
electricity
has
emerged
as
a
highly
promising
solution
alleviate
shortages
achieve
neutrality.
Among
these
C2+
products,
ethylene
(C2H4)
holds
particular
importance
in
petrochemical
industry.
Accordingly,
this
review
aims
establish
connection
between
fundamentals
(CO2RR-to-C2H4)
laboratory-scale
research
(lab)
its
potential
applications
industrial-level
fabrication
(fab).
begins
summarizing
fundamental
aspects,
including
design
strategies
high-performance
Cu-based
electrocatalysts
advanced
electrolyzer
devices.
Subsequently,
innovative
techniques
presented
address
inherent
challenges
encountered
during
implementations
CO2RR-to-C2H4
industrial
scenarios.
Additionally,
case
studies
techno-economic
analysis
process
discussed,
taking
into
factors
such
cost-effectiveness,
scalability,
market
potential.
concludes
outlining
perspectives
associated
with
scaling
up
process.
insights
expected
make
valuable
contribution
advancing
from
lab
fab.
