Small,
Journal Year:
2024,
Volume and Issue:
20(29)
Published: Feb. 11, 2024
Functional
nanostructures
build
up
a
basis
for
the
future
materials
and
devices,
providing
wide
variety
of
functionalities,
possibility
designing
bio-compatible
nanoprobes,
etc.
However,
development
new
nanostructured
via
trial-and-error
approach
is
obviously
limited
by
laborious
efforts
on
their
syntheses,
cost
manpower.
This
one
reasons
an
increasing
interest
in
design
novel
with
required
properties
assisted
machine
learning
approaches.
Here,
dataset
synthetic
parameters
optical
important
class
light-emitting
nanomaterials
-
carbon
dots
are
collected,
processed,
analyzed
transitions
red
near-infrared
spectral
ranges.
A
model
prediction
characteristics
these
based
multiple
linear
regression
established
verified
comparison
predicted
experimentally
observed
synthesized
three
different
laboratories.
Based
analysis,
open-source
code
provided
to
be
used
researchers
procedures.
Nutrition Reviews,
Journal Year:
2022,
Volume and Issue:
80(12), P. 2288 - 2300
Published: April 14, 2022
In
the
late
2010s,
artificial
intelligence
(AI)
technologies
became
complementary
to
research
areas
of
food
science
and
nutrition.
This
review
aims
summarize
these
technological
advances
by
systematically
describing
following:
use
AI
in
other
fields
(eg,
engineering,
pharmacy,
medicine);
history
relation
nutrition;
currently
used
agricultural
industries;
some
important
applications
such
as
immunity-boosting
foods,
dietary
assessment,
gut
microbiome
profile
analysis,
toxicity
prediction
ingredients.
These
are
likely
be
great
demand
near
future.
can
provide
a
starting
point
for
brainstorming
generating
new
nutrition
that
have
yet
imagined.
Engineering Science & Technology Journal,
Journal Year:
2023,
Volume and Issue:
4(3), P. 66 - 83
Published: Sept. 11, 2023
This
research
paper
examines
the
transformative
role
of
artificial
intelligence
(AI)
and
machine
learning
(ML)
in
advancing
materials
discovery
production
processes.
The
explores
historical
evolution
AI
ML
techniques,
their
application
science,
challenges
limitations,
emerging
technologies,
ethical
considerations.
Key
findings
highlight
how
accelerate
discovery,
optimize
processes,
enhance
quality
control.
Emerging
technologies
such
as
generative
models,
reinforcement
learning,
integration
with
experimental
techniques
are
discussed.
Ethical
considerations
encompass
data
privacy,
intellectual
property,
job
displacement,
bias
mitigation,
transparency,
human-AI
collaboration.
implications
for
future
underscore
profound
impact
on
enabling
faster
efficient
production,
novel
material
development.
Keywords:
Artificial
Intelligence,
Machine
Learning,
Materials
Discovery,
Production,
Generative
Models,
Reinforcement
Data
Privacy,
Considerations.
Journal of Materials Chemistry A,
Journal Year:
2023,
Volume and Issue:
11(8), P. 3904 - 3936
Published: Jan. 1, 2023
This
review
compares
machine
learning
approaches
for
property
prediction
of
materials,
optimization,
and
energy
storage
device
health
estimation.
Current
challenges
prospects
high-impact
areas
in
research
are
highlighted.
IEEE Access,
Journal Year:
2023,
Volume and Issue:
11, P. 71407 - 71425
Published: Jan. 1, 2023
Over
the
last
ten
years,
field
of
dermatology
has
experienced
significant
advancements
through
utilization
artificial
intelligence
(AI)
technologies.
The
adoption
such
technologies
is
multifaceted,
encompassing
tasks
as
screening,
diagnosis,
treatment,
and
prediction
treatment
outcomes.
majority
prior
systematic
reviews
in
this
domain
were
centered
on
medical
dermatology,
with
aim
detecting
managing
serious
skin
diseases
cancer.
However,
AI
cosmetic
which
focuses
improving
conditions
for
purposes,
not
been
comprehensively
reviewed.
Therefore,
objective
review
article
to
analyze
existing
recent
research
revolving
around
applications
dermatology.
study
encompasses
articles
published
between
2018
2023,
where
a
total
63
publications
are
deemed
relevant
based
established
inclusion
criteria,
divided
into
five
categories
domains,
namely
product
development,
assessment,
condition
recommendation,
outcome
prediction.
This
provides
only
valuable
insights
researchers
interested
exploring
new
areas
related
aesthetic
medicine
but
also
applicable
guidance
practitioners
seeking
implement
address
real-world
challenges
services.
Small,
Journal Year:
2024,
Volume and Issue:
20(29)
Published: Feb. 11, 2024
Functional
nanostructures
build
up
a
basis
for
the
future
materials
and
devices,
providing
wide
variety
of
functionalities,
possibility
designing
bio-compatible
nanoprobes,
etc.
However,
development
new
nanostructured
via
trial-and-error
approach
is
obviously
limited
by
laborious
efforts
on
their
syntheses,
cost
manpower.
This
one
reasons
an
increasing
interest
in
design
novel
with
required
properties
assisted
machine
learning
approaches.
Here,
dataset
synthetic
parameters
optical
important
class
light-emitting
nanomaterials
-
carbon
dots
are
collected,
processed,
analyzed
transitions
red
near-infrared
spectral
ranges.
A
model
prediction
characteristics
these
based
multiple
linear
regression
established
verified
comparison
predicted
experimentally
observed
synthesized
three
different
laboratories.
Based
analysis,
open-source
code
provided
to
be
used
researchers
procedures.
