Experiment
planning
algorithms
are
a
required
component
of
autonomous
platforms
for
scientific
discovery.
Selecting
suitable
optimization
algorithm
novel
application
is
an
important
yet
difficult
choice
researcher
has
to
make
based
on
past
empirical
performance
similar
tasks.
To
facilitate
the
evaluation
various
chemistry
and
materials
science
tasks,
we
previously
introduced
OLYMPUS
(Mach.
Learn.:
Sci.
Technol.
2,
035021,
2021),
Python
package
providing
consistent
easy-to-use
interface
numerous
benchmark
datasets.
While
original
was
limited
continuous
parameters
single
objectives,
in
this
work
expand
OLYMPUS'
capabilities
include
mixed
(continuous,
discrete,
categorical)
parameter
types
multiple
objectives.
Several
experiment
already
contained
extended
handle
categorical
discrete
types,
five
additional
planners
implemented
(23
total).
We
also
provide
23
datasets
taken
from
literature
(33
total),
covering
wide
range
research
areas,
chemical
reaction
manufacturing.
Finally,
visualization
enhanced
allow
easy
inspection
results,
core
functionality
embedded
Streamlit
web
code-free
usage.
demonstrate
how
enables
researchers
rapidly
different
strategies
gain
insight
into
their
behavior
by
focusing
two
case
studies:
Suzuki-Miyaura
cross-coupling
with
conditions,
multi-objective
redox-active
materials.
The
updated
provides
practitioners
large
suite
tools
efficiently
analyze
mixed-parameter
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.
Digital Discovery,
Journal Year:
2023,
Volume and Issue:
3(1), P. 23 - 33
Published: Dec. 6, 2023
The
ASLLA
Symposium
focused
on
accelerating
chemical
science
with
AI.
Discussions
data,
new
applications,
algorithms,
and
education
were
summarized.
Recommendations
for
researchers,
educators,
academic
bodies
provided.
Nature Communications,
Journal Year:
2023,
Volume and Issue:
14(1)
Published: July 3, 2023
High-throughput
experimentation
(HTE)
is
an
increasingly
important
tool
in
reaction
discovery.
While
the
hardware
for
running
HTE
chemical
laboratory
has
evolved
significantly
recent
years,
there
remains
a
need
software
solutions
to
navigate
data-rich
experiments.
Here
we
have
developed
phactor™,
that
facilitates
performance
and
analysis
of
laboratory.
phactor™
allows
experimentalists
rapidly
design
arrays
reactions
or
direct-to-biology
experiments
24,
96,
384,
1,536
wellplates.
Users
can
access
online
reagent
data,
such
as
inventory,
virtually
populate
wells
with
produce
instructions
perform
array
manually,
assistance
liquid
handling
robot.
After
completion
array,
analytical
results
be
uploaded
facile
evaluation,
guide
next
series
All
metadata,
are
stored
machine-readable
formats
readily
translatable
various
software.
We
also
demonstrate
use
discovery
several
chemistries,
including
identification
low
micromolar
inhibitor
SARS-CoV-2
main
protease.
Furthermore,
been
made
available
free
academic
24-
96-well
via
interface.
Chemical Reviews,
Journal Year:
2024,
Volume and Issue:
124(9), P. 5421 - 5469
Published: April 25, 2024
Utilization
of
renewable
energies
for
catalytically
generating
value-added
chemicals
is
highly
desirable
in
this
era
rising
energy
demands
and
climate
change
impacts.
Artificial
photosynthetic
systems
or
photocatalysts
utilize
light
to
convert
abundant
CO2,
H2O,
O2
fuels,
such
as
carbohydrates
hydrogen,
thus
converting
storable
chemical
resources.
The
emergence
intense
X-ray
pulses
from
synchrotrons,
ultrafast
free
electron
lasers,
table-top
laser-driven
sources
over
the
past
decades
opens
new
frontiers
deciphering
photoinduced
catalytic
reaction
mechanisms
on
multiple
temporal
spatial
scales.
Operando
spectroscopic
methods
offer
a
set
electronic
transitions
probing
oxidation
states,
coordinating
geometry,
spin
states
metal
center
photosensitizers
with
unprecedented
time
resolution.
scattering
enable
previously
elusive
steps
be
characterized
different
length
scales
methodological
progress
their
application
examples
collected
review
will
glimpse
into
accomplishments
current
state
both
natural
synthetic
systems.
Looking
forward,
there
are
still
many
challenges
opportunities
at
frontier
research
that
require
further
advancement
characterization
techniques.
Journal of the American Chemical Society,
Journal Year:
2024,
Volume and Issue:
146(29), P. 19654 - 19659
Published: July 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.
