Nature Communications,
Journal Year:
2024,
Volume and Issue:
15(1)
Published: July 25, 2024
Exploring
catalytic
reaction
mechanisms
is
crucial
for
understanding
chemical
processes,
optimizing
conditions,
and
developing
more
effective
catalysts.
We
present
a
reaction-agnostic
framework
based
on
high-throughput
deep
reinforcement
learning
with
first
principles
(HDRL-FP)
that
offers
excellent
generalizability
investigating
reactions.
HDRL-FP
introduces
generalizable
representation
of
reactions
constructed
solely
from
atomic
positions,
which
are
subsequently
mapped
to
first-principles-derived
potential
energy
landscapes.
By
leveraging
thousands
simultaneous
simulations
single
GPU,
enables
rapid
convergence
the
optimal
path
at
low
cost.
Its
effectiveness
demonstrated
through
studies
hydrogen
nitrogen
migration
in
Haber-Bosch
ammonia
synthesis
Fe(111)
surface.
Our
findings
reveal
Langmuir-Hinshelwood
mechanism
shares
same
transition
state
as
Eley-Rideal
H
NH
Journal of the American Chemical Society,
Journal Year:
2023,
Volume and Issue:
145(40), P. 21699 - 21716
Published: Sept. 27, 2023
Exceptional
molecules
and
materials
with
one
or
more
extraordinary
properties
are
both
technologically
valuable
fundamentally
interesting,
because
they
often
involve
new
physical
phenomena
compositions
that
defy
expectations.
Historically,
exceptionality
has
been
achieved
through
serendipity,
but
recently,
machine
learning
(ML)
automated
experimentation
have
widely
proposed
to
accelerate
target
identification
synthesis
planning.
In
this
Perspective,
we
argue
the
data-driven
methods
commonly
used
today
well-suited
for
optimization
not
realization
of
exceptional
molecules.
Finding
such
outliers
should
be
possible
using
ML,
only
by
shifting
away
from
traditional
ML
approaches
tweak
composition,
crystal
structure,
reaction
pathway.
We
highlight
case
studies
high-Tc
oxide
superconductors
superhard
demonstrate
challenges
ML-guided
discovery
discuss
limitations
automation
task.
then
provide
six
recommendations
development
capable
discovery:
(i)
Avoid
tyranny
middle
focus
on
extrema;
(ii)
When
data
limited,
qualitative
predictions
direction
than
interpolative
accuracy;
(iii)
Sample
what
can
made
how
make
it
defer
optimization;
(iv)
Create
room
(and
look)
unexpected
while
pursuing
your
goal;
(v)
Try
fill-in-the-blanks
input
output
space;
(vi)
Do
confuse
human
understanding
model
interpretability.
conclude
a
description
these
integrated
into
workflows,
which
enable
materials.
ACS Central Science,
Journal Year:
2023,
Volume and Issue:
9(12), P. 2196 - 2204
Published: Dec. 8, 2023
Models
can
codify
our
understanding
of
chemical
reactivity
and
serve
a
useful
purpose
in
the
development
new
synthetic
processes
via,
for
example,
evaluating
hypothetical
reaction
conditions
or
silico
substrate
tolerance.
Perhaps
most
determining
factor
is
composition
training
data
whether
it
sufficient
to
train
model
that
make
accurate
predictions
over
full
domain
interest.
Here,
we
discuss
design
datasets
ways
are
conducive
data-driven
modeling,
emphasizing
idea
set
diversity
generalizability
rely
on
choice
molecular
representation.
We
additionally
experimental
constraints
associated
with
generating
common
types
chemistry
how
these
considerations
should
influence
dataset
building.
Journal of Chemical Information and Modeling,
Journal Year:
2023,
Volume and Issue:
63(15), P. 4505 - 4532
Published: July 19, 2023
The
field
of
computational
chemistry
has
seen
a
significant
increase
in
the
integration
machine
learning
concepts
and
algorithms.
In
this
Perspective,
we
surveyed
179
open-source
software
projects,
with
corresponding
peer-reviewed
papers
published
within
last
5
years,
to
better
understand
topics
being
investigated
by
approaches.
For
each
project,
provide
short
description,
link
code,
accompanying
license
type,
whether
training
data
resulting
models
are
made
publicly
available.
Based
on
those
deposited
GitHub
repositories,
most
popular
employed
Python
libraries
identified.
We
hope
that
survey
will
serve
as
resource
learn
about
or
specific
architectures
thereof
identifying
accessible
codes
topic
basis.
To
end,
also
include
for
generating
fundamental
learning.
our
observations
considering
three
pillars
collaborative
work,
open
data,
source
(code),
models,
some
suggestions
community.
Faraday Discussions,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Jan. 1, 2024
Automated
synthesis
planning
has
recently
re-emerged
as
a
research
area
at
the
intersection
of
chemistry
and
machine
learning.
Despite
appearance
steady
progress,
we
argue
that
imperfect
benchmarks
inconsistent
comparisons
mask
systematic
shortcomings
existing
techniques,
unnecessarily
hamper
progress.
To
remedy
this,
present
library
with
an
extensive
benchmarking
framework,
called
SYNTHESEUS,
which
promotes
best
practice
by
default,
enabling
consistent
meaningful
evaluation
single-step
multi-step
algorithms.
We
demonstrate
capabilities
SYNTHESEUS
re-evaluating
several
previous
retrosynthesis
algorithms,
find
ranking
state-of-the-art
models
changes
in
controlled
experiments.
end
guidance
for
future
works
this
area,
call
on
community
to
engage
discussion
how
improve
planning.
Organic Process Research & Development,
Journal Year:
2023,
Volume and Issue:
27(8), P. 1510 - 1516
Published: Aug. 1, 2023
High-throughput
experimentation
is
a
common
practice
in
the
optimization
of
chemical
synthesis.
Chemists
design
reaction
arrays
to
optimize
yield
couplings
between
building
blocks.
Popular
reactions
used
pharmaceutical
research
include
amide
coupling,
Suzuki
and
Buchwald–Hartwig
coupling.
We
show
how
artificial
intelligence
(AI)
language
model
ChatGPT
can
automatically
formulate
for
these
based
on
literature
corpus
it
was
trained
on.
Critically,
we
showcase
results
be
directly
translated
into
inputs
management
software
phactor,
which
enables
automated
execution
analysis
assays.
This
workflow
experimentally
demonstrated,
with
modest
excellent
yields
products
obtained
each
instance
first
attempt.
Chemical Science,
Journal Year:
2023,
Volume and Issue:
15(2), P. 500 - 510
Published: Dec. 5, 2023
We
evaluate
the
effectiveness
of
fine-tuning
GPT-3
for
prediction
electronic
and
functional
properties
organic
molecules.
Our
findings
show
that
fine-tuned
can
successfully
identify
distinguish
between
chemically
meaningful
patterns,
discern
subtle
differences
among
them,
exhibiting
robust
predictive
performance
molecular
properties.
focus
on
assessing
models'
resilience
to
information
loss,
resulting
from
absence
atoms
or
chemical
groups,
noise
we
introduce
Journal of Chemical Information and Modeling,
Journal Year:
2023,
Volume and Issue:
64(1), P. 42 - 56
Published: Dec. 20, 2023
Machine
Learning
(ML)
techniques
face
significant
challenges
when
predicting
advanced
chemical
properties,
such
as
yield,
feasibility
of
synthesis,
and
optimal
reaction
conditions.
These
stem
from
the
high-dimensional
nature
prediction
task
myriad
essential
variables
involved,
ranging
reactants
reagents
to
catalysts,
temperature,
purification
processes.
Successfully
developing
a
reliable
predictive
model
not
only
holds
potential
for
optimizing
high-throughput
experiments
but
can
also
elevate
existing
retrosynthetic
approaches
bolster
plethora
applications
within
field.
In
this
review,
we
systematically
evaluate
efficacy
current
ML
methodologies
in
chemoinformatics,
shedding
light
on
their
milestones
inherent
limitations.
Additionally,
detailed
examination
representative
case
study
provides
insights
into
prevailing
issues
related
data
availability
transferability
discipline.
Journal of the American Chemical Society,
Journal Year:
2023,
Volume and Issue:
145(28), P. 15414 - 15424
Published: July 6, 2023
Owing
to
the
unknown
correlation
of
a
metal’s
ligand
and
its
resulting
preferred
speciation
in
terms
oxidation
state,
geometry,
nuclearity,
rational
design
multinuclear
catalysts
remains
challenging.
With
goal
accelerate
identification
suitable
ligands
that
form
trialkylphosphine-derived
dihalogen-bridged
Ni(I)
dimers,
we
herein
employed
an
assumption-based
machine
learning
approach.
The
workflow
offers
guidance
space
for
desired
without
(or
only
minimal)
prior
experimental
data
points.
We
experimentally
verified
predictions
synthesized
numerous
novel
dimers
as
well
explored
their
potential
catalysis.
demonstrate
C–I
selective
arylations
polyhalogenated
arenes
bearing
competing
C–Br
C–Cl
sites
under
5
min
at
room
temperature
using
0.2
mol
%
newly
developed
dimer,
[Ni(I)(μ-Br)PAd2(n-Bu)]2,
which
is
so
far
unmet
with
alternative
dinuclear
or
mononuclear
Ni
Pd
catalysts.
Advanced Energy Materials,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: Nov. 12, 2023
Abstract
Metal
cation‐doped
lead
halide
perovskite
(LHP)
quantum
dots
(QDs)
with
photoluminescence
yields
(PLQYs)
higher
than
unity,
due
to
cutting
phenomena,
are
an
important
building
block
of
the
next‐generation
renewable
energy
technologies.
However,
synthetic
route
exploration
and
development
highest‐performing
QDs
for
device
applications
remain
challenging.
In
this
work,
Smart
Dope
is
presented,
which
a
self‐driving
fluidic
lab
(SDFL),
accelerated
synthesis
space
autonomous
optimization
LHP
QDs.
Specifically,
multi‐cation
doping
CsPbCl
3
using
one‐pot
high‐temperature
chemistry
reported.
continuously
synthesizes
multi‐cation‐doped
high‐pressure
gas‐liquid
segmented
flow
format
enable
continuous
experimentation
minimal
experimental
noise
at
reaction
temperatures
up
255°C.
offers
multiple
functionalities,
including
mechanistic
studies
through
digital
twin
QD
modeling,
closed‐loop
discovery,
on‐demand
manufacturing
high‐performing
Through
these
developments,
autonomously
identifies
optimal
Mn‐Yb
co‐doped
PLQY
158%,
highest
reported
value
class
date.
illustrates
power
SDFLs
in
accelerating
discovery
emerging
advanced
materials.