Recent
advancements
in
artificial
intelligence
(AI)-based
molecular
design
methodologies
have
offered
synthetic
chemists
new
ways
to
functional
molecules
with
their
desired
properties.
While
various
AI-based
molecule
generators
significantly
advanced
toward
practical
applications,
effective
use
still
requires
specialized
knowledge
and
skills
concerning
AI
techniques.
Here,
we
develop
a
large
language
model
(LLM)-powered
chatbot,
ChatChemTS,
that
enables
using
an
generator
through
only
chat
interactions,
including
automated
construction
of
reward
functions
for
the
specified
Our
study
showcases
utility
ChatChemTS
de
novo
cases
involving
chromophores
anticancer
drugs
(epidermal
growth
factor
receptor
inhibitors),
exemplifying
single-
multiobjective
optimization
scenarios,
respectively.
is
provided
as
open-source
package
on
GitHub
at
https://github.com/molecule-generator-collection/ChatChemTS.
Chemical Engineering Journal,
Год журнала:
2024,
Номер
490, С. 151625 - 151625
Опубликована: Апрель 24, 2024
In
the
rapidly
evolving
landscape
of
electrochemical
energy
storage
(EES),
advent
artificial
intelligence
(AI)
has
emerged
as
a
keystone
for
innovation
in
material
design,
propelling
forward
design
and
discovery
batteries,
fuel
cells,
supercapacitors,
many
other
functional
materials.
This
review
paper
elucidates
burgeoning
role
AI
materials
from
foundational
machine
learning
(ML)
techniques
to
its
current
pivotal
advancing
frontiers
science
storage,
including
enhancing
performance,
durability,
safety
battery
technologies,
cell
efficiency
longevity,
fine-tuning
supercapacitors
achieve
superior
capabilities.
Collectively,
we
present
comprehensive
overview
recent
advancements
that
have
significantly
accelerated
development
next-generation
EES,
offering
insights
into
future
research
trajectories
potential
unlock
new
horizons
science.
Journal of Chemical Education,
Год журнала:
2024,
Номер
101(6), С. 2475 - 2482
Опубликована: Май 22, 2024
The
rapid
integration
of
generative
artificial
intelligence
(AI)
into
educational
settings
prompts
an
urgent
examination
its
efficacy
and
the
strategies
that
students
employ
to
harness
potential.
This
study
focuses
on
preservice
chemistry
teachers
use
AI
for
chemistry-specific
problem-solving
task
completion.
We
found
there
is
a
prevalent
reliance
copy-pasting
tactics
in
initial
prompting
approaches,
need
guidance
improve
their
abilities.
By
implementing
"Five
S"
framework,
we
explore
evolution
student
resultant
satisfaction
with
AI-generated
responses.
Our
findings
indicate
that,
while
initially
struggle
nuances
effective
prompting,
adoption
structured
frameworks
significantly
enhances
perceived
quality
answers.
research
sheds
light
current
state
among
but
also
underscores
importance
targeted
refine
interaction
academic
contexts.
In
particular,
suggest
critical
engagement
methodological
prompt
engineering
maximize
benefits
technologies.
Chemical Science,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 9, 2024
Large
language
models
(LLMs)
have
emerged
as
powerful
tools
in
chemistry,
significantly
impacting
molecule
design,
property
prediction,
and
synthesis
optimization.
This
review
highlights
LLM
capabilities
these
domains
their
potential
to
accelerate
scientific
discovery
through
automation.
We
also
LLM-based
autonomous
agents:
LLMs
with
a
broader
set
of
interact
surrounding
environment.
These
agents
perform
diverse
tasks
such
paper
scraping,
interfacing
automated
laboratories,
planning.
As
are
an
emerging
topic,
we
extend
the
scope
our
beyond
chemistry
discuss
across
any
domains.
covers
recent
history,
current
capabilities,
design
agents,
addressing
specific
challenges,
opportunities,
future
directions
chemistry.
Key
challenges
include
data
quality
integration,
model
interpretability,
need
for
standard
benchmarks,
while
point
towards
more
sophisticated
multi-modal
enhanced
collaboration
between
experimental
methods.
Due
quick
pace
this
field,
repository
has
been
built
keep
track
latest
studies:
https://github.com/ur-whitelab/LLMs-in-science.
Journal of the American Chemical Society,
Год журнала:
2024,
Номер
146(29), С. 19654 - 19659
Опубликована: Июль 11, 2024
We
evaluate
the
effectiveness
of
pretrained
and
fine-tuned
large
language
models
(LLMs)
for
predicting
synthesizability
inorganic
compounds
selection
precursors
needed
to
perform
synthesis.
The
predictions
LLMs
are
comparable
to─and
sometimes
better
than─recent
bespoke
machine
learning
these
tasks
but
require
only
minimal
user
expertise,
cost,
time
develop.
Therefore,
this
strategy
can
serve
both
as
an
effective
strong
baseline
future
studies
various
chemical
applications
a
practical
tool
experimental
chemists.
Chem & Bio Engineering,
Год журнала:
2025,
Номер
2(4), С. 210 - 228
Опубликована: Март 5, 2025
As
the
chemical
industry
shifts
toward
sustainable
practices,
there
is
a
growing
initiative
to
replace
conventional
fossil-derived
solvents
with
environmentally
friendly
alternatives
such
as
ionic
liquids
(ILs)
and
deep
eutectic
(DESs).
Artificial
intelligence
(AI)
plays
key
role
in
discovery
design
of
novel
development
green
processes.
This
review
explores
latest
advancements
AI-assisted
solvent
screening
specific
focus
on
machine
learning
(ML)
models
for
physicochemical
property
prediction
separation
process
design.
Additionally,
this
paper
highlights
recent
progress
automated
high-throughput
(HT)
platforms
screening.
Finally,
discusses
challenges
prospects
ML-driven
HT
strategies
optimization.
To
end,
provides
insights
advance
future
Atomically
precise
metal
nanoclusters
(MNCs)
represent
a
fascinating
class
of
ultrasmall
nanoparticles
with
molecule-like
properties,
bridging
conventional
metal-ligand
complexes
and
nanocrystals.
Despite
their
potential
for
various
applications,
synthesis
challenges
such
as
understanding
varied
synthetic
parameters
property-driven
persist,
hindering
full
exploitation
wider
application.
Incorporating
smart
methodologies,
including
closed-loop
framework
automation,
data
interpretation,
feedback
from
AI,
offers
promising
solutions
to
address
these
challenges.
In
this
perspective,
we
summarize
the
that
has
been
demonstrated
in
nanomaterials
explore
research
frontiers
MNCs.
Moreover,
perspectives
on
inherent
opportunities
MNCs
are
discussed,
aiming
provide
insights
directions
future
advancements
emerging
field
AI
Science,
while
integration
deep
learning
algorithms
stands
substantially
enrich
by
offering
enhanced
predictive
capabilities,
optimization
strategies,
control
mechanisms,
thereby
extending
MNC
synthesis.
Journal of Cheminformatics,
Год журнала:
2025,
Номер
17(1)
Опубликована: Март 24, 2025
Abstract
Recent
advancements
in
artificial
intelligence
(AI)-based
molecular
design
methodologies
have
offered
synthetic
chemists
new
ways
to
functional
molecules
with
their
desired
properties.
While
various
AI-based
molecule
generators
significantly
advanced
toward
practical
applications,
effective
use
still
requires
specialized
knowledge
and
skills
concerning
AI
techniques.
Here,
we
develop
a
large
language
model
(LLM)-powered
chatbot,
ChatChemTS,
that
assists
users
designing
using
an
generator
through
only
chat
interactions,
including
automated
construction
of
reward
functions
for
the
specified
Our
study
showcases
utility
ChatChemTS
de
novo
cases
involving
chromophores
anticancer
drugs
(epidermal
growth
factor
receptor
inhibitors),
exemplifying
single-
multiobjective
optimization
scenarios,
respectively.
is
provided
as
open-source
package
on
GitHub
at
https://github.com/molecule-generator-collection/ChatChemTS
.
Scientific
contribution
application
utilizing
generator,
ChemTSv2,
solely
interactions.
This
demonstrates
LLMs
possess
potential
utilize
software,
such
generators,
which
require
technical
skills.
