Discoveries
of
novel
electrolyte-electrode
combinations
require
comprehensive
structure-property-interface
correlations.
Herein,
we
present
an
autonomous
millimeter
scale
high-throughput
battery
research
system
(MISCHBARES)
operated
by
hierarchical
laboratory
automation
and
orchestration
(HELAO)
which
integrates
modular
instrumentation
AI
control.
This
paper
will
cathode
electrolyte
interphase
(CEI)
formation
in
lithium-ion
batteries
at
various
potentials
correlating
electrochemistry
spectroscopy.
We
believe
quality
control
complex
data
analysis
to
be
the
missing
puzzle
piece
towards
more
workflow
automation.
Auto-MISCHBARES
automatic
for
both
hardware
software
ensure
high
reliability
through
on-the-fly
fidelity
assessment
each
individual
experiment.
Data
is
achieved
our
Modular
Autonomous
Analysis
Platform
(MADAP)
presented
platform,
capable
performing
a
fully
automated
voltammetry
measurements
real-time.
Integration
MISCHBARES
MADAP
HELAO
enables
versatile
active
learning
workflows
discovery
new
materials.
demonstrate
this
integrated
reliable
charging/discharging
protocols.
Digital Discovery,
Journal Year:
2024,
Volume and Issue:
3(5), P. 883 - 895
Published: Jan. 1, 2024
The
high-throughput
Auto-MISCHBARES
platform
streamlines
reliable
autonomous
experimentation
across
laboratory
devices
through
scheduling,
quality
control,
live
feedback,
and
real-time
data
management,
including
measurement,
validation
analysis.
The
beginning
and
ripening
of
digital
chemistry
is
analyzed
focusing
on
the
role
artificial
intelligence
(AI)
in
an
expected
leap
chemical
sciences
to
bring
this
area
next
evolutionary
level.
analytic
description
selects
highlights
top
20
AI-based
technologies
7
broader
themes
that
are
reshaping
field.
It
underscores
integration
tools
such
as
machine
learning,
big
data,
twins,
Internet
Things
(IoT),
robotic
platforms,
smart
control
processes,
virtual
reality
blockchain,
among
many
others,
enhancing
research
methods,
educational
approaches,
industrial
practices
chemistry.
significance
study
lies
its
focused
overview
how
these
innovations
foster
a
more
efficient,
sustainable,
innovative
future
sciences.
This
article
not
only
illustrates
transformative
impact
but
also
draws
new
pathways
chemistry,
offering
broad
appeal
researchers,
educators,
industry
professionals
embrace
advancements
for
addressing
contemporary
challenges
The Journal of Physical Chemistry Letters,
Journal Year:
2024,
Volume and Issue:
15(22), P. 5978 - 5984
Published: May 30, 2024
Recent
decades
have
witnessed
the
rapid
development
of
autonomous
laboratories
and
artificial
intelligence,
where
experiments
can
be
automatically
run
optimized.
Although
human
work
is
reduced,
total
time
experimental
optimization
still
consuming
due
to
limitations
current
ab
metaverse
framework,
which
accurately
predicts
future
state
system
by
receiving
analyzing
in
situ
data.
To
substitute
for
traditional
simulation
methods,
we
designed
a
physically
endorsed
deep
learning
model
predict
picture
ranging
from
atomic
image
bulk
appearance,
intensively
using
correlations
between
properties
system.
Through
this
studied
general
aqueous
system,
covering
100+
common
ionic
solutions.
We
simulate
as
well
solvation
compounds
ahead
real
experiments.
In
way,
optimized
more
efficiently
without
waiting
end
bad
iteration.
hope
our
offers
fresh
direction
digitization
chemical
information,
enhancing
access
use
data
advancing
field
physical
chemistry.
Discoveries
of
novel
electrolyte-electrode
combinations
require
comprehensive
structure-property-interface
correlations.
Herein,
we
present
an
autonomous
millimeter
scale
high-throughput
battery
research
system
(MISCHBARES)
operated
by
hierarchical
laboratory
automation
and
orchestration
(HELAO)
which
integrates
modular
instrumentation
AI
control.
This
paper
will
cathode
electrolyte
interphase
(CEI)
formation
in
lithium-ion
batteries
at
various
potentials
correlating
electrochemistry
spectroscopy.
We
believe
quality
control
complex
data
analysis
to
be
the
missing
puzzle
piece
towards
more
workflow
automation.
Auto-MISCHBARES
automatic
for
both
hardware
software
ensure
high
reliability
through
on-the-fly
fidelity
assessment
each
individual
experiment.
Data
is
achieved
our
Modular
Autonomous
Analysis
Platform
(MADAP)
presented
platform,
capable
performing
a
fully
automated
voltammetry
measurements
real-time.
Integration
MISCHBARES
MADAP
HELAO
enables
versatile
active
learning
workflows
discovery
new
materials.
demonstrate
this
integrated
reliable
charging/discharging
protocols.
Discoveries
of
novel
electrolyte-electrode
combinations
require
comprehensive
structure-property-interface
correlations.
Herein,
we
present
an
autonomous
millimeter
scale
high-throughput
battery
research
system
(MISCHBARES)
operated
by
hierarchical
laboratory
automation
and
orchestration
(HELAO)
which
integrates
modular
instrumentation
AI
control.
This
paper
will
cathode
electrolyte
interphase
(CEI)
formation
in
lithium-ion
batteries
at
various
potentials
correlating
electrochemistry
spectroscopy.
We
believe
quality
control
complex
data
analysis
to
be
the
missing
puzzle
piece
towards
more
workflow
automation.
Auto-MISCHBARES
automatic
for
both
hardware
software
ensure
high
reliability
through
on-the-fly
fidelity
assessment
each
individual
experiment.
Data
is
achieved
our
Modular
Autonomous
Analysis
Platform
(MADAP)
presented
platform,
capable
performing
a
fully
automated
voltammetry
measurements
real-time.
Integration
MISCHBARES
MADAP
HELAO
enables
versatile
active
learning
workflows
discovery
new
materials.
demonstrate
this
integrated
reliable
charging/discharging
protocols.
