►
From YouTube: The Materials Genome as a Pathfinder Part 4
Description
Enabling High Dimensional Embedded Multi Functional System
A
A
He
is
in
the
physics
and
computational
technologies,
research
area
in
etl,
with
research
portfolio
that
spans
materials,
manufacturing
information
technologies,
quantum
sensing
lays
their
material
interactions
and
before
joining
the
advanced
technology,
lab
rick
has
worked
at
several
other
lucky
business
units,
including
their
space
systems,
advanced
technology
center,
where
he
was
a
key
member
of
the
research
team,
developing
wide
broadband
and
nonlinear
optical
polym
optical
polymer
modulators
for
RF
communications.
So
then
his
background
is
in
chemical
engineering
at
PhD,
in
material
science
from
Stanford
and
focused
on
near-ir
optical
lost,
structure-property
relationship.
B
You
thanks
to
Dave
in
Syria
for
vitami.
Oh
yeah
I
really
appreciate
it.
I'm
really
excited
about
the
opportunity
presented
by
the
big
data
hub
here.
I
have
I
expect.
We
will
be
a
a
strong
supporter
and
participator
and
I
thought
today.
I
really
want
to
thank
the
previous
speakers
because
he
has
really
said
set
this
talk
up
for
me
nicely
and
I
will
have
will
be
able
to
speed
through
some
of
this,
but
then
I
want
to
also
give
prompt
to
our
friends
from
pratt
whitney
a
lot
of
those
systems
you
saw
up.
B
B
The
Lockheed
Martin
is
sort
of
a
federated
as
a
federated
business
structure.
We
have
these
four
primary
business
areas,
Aeronautics
it's
just
more
well
known
to
most
of
you,
do
all
manner
of
third
fourth,
fifth
generation
fighter
systems,
their
transport
missiles
and
fire
control.
These
are
it's
a
pretty
diversified
business
areas
spanning
fire
control
systems
seekers,
tactical
missiles.
B
B
B
Our
laboratory
is
chartered
with
looking
way
out
on
the
horizon
ahead
of
the
those
for
business
areas.
I
showed
you
for
the
most
part.
We
do
not
touch
product.
We,
we
are
the
sort
of
searchlight
much
the
way
DARPA.
Is
we
look
out
generation
after
next
technologies,
both
for
discriminating
new
capabilities,
as
well
as
understanding
the
risk
of
new
technologies
for
for
our
businesses.
B
B
We're
seeing
great
examples
of
that
from
what
we
fell
in
the
previous
talks,
making
digital
data
more
accessible
and
creating
the
next
generation
materials
workforce,
many
of
which
you
folks
in
the
room
here
are
going
to
be
responsible
for
so
this
is
a
teen
different
versions
of
this
from
the
other
industry,
folks,
Siemens
and
Pratt
&,
Whitney
why's.
It
take
so
long
for
us
to
get
to
new
materials
in
our
systems.
Well,
this
is
a
very,
very
overly
simplified
version
of
the
material
to
manufacturing
design
process.
B
We
spend
a
significant
amount
of
time
going
through
a
conceptual
structure,
design
material
selection,
material
qualification
if
it
were
only
that
simple
and
in
costly
which
almost
bears
at
that
costs
and
and
make
it
inside
the
windows
for
new
new
system
or
platform
design.
But
this
right
hand
side
of
this
process,
process,
verification,
assembly,
integration,
tests,
heart
certification,
structure,
certification,
leading
into
low
rate
production.
That's
really
where
significant
costs
occur,
on
the
order
of
50
to
100
million
dollars
spanning
up
to
ten
years.
That's
what
we're
trying
to
a
short
circuit
via
the
material
genome.
B
There's
a
lot
of
resistance
to
adoption
of
new
materials
we
see
at
as
manufacturing
as
a
offering
a
huge
potential
to
allow
us
to
adapt
rapidly
to
our
customers,
changing
threats
or
needs
with
highly
new,
highly
integrated
functionality,
embedded
functionality
in
the
same
structure.
Designing
our
thermal
optical
RF
structural
systems,
together
into
more
tightly
integrated
components.
B
B
The
Amazon
UPS's
of
the
world
are
only
going
to
keep
expanding
and
creating
new
new
capabilities
for
us
to
tap
into
to
enable
things
like
drop
shipments
to
forward
operating
bases
or
forward
deployments
to
be
able
to
drop
ship
things
like
intermediate.
Build
custom,
tooling
cut
some
feedstocks
to
a
terminal
manufacturing
locations.
B
So
what
does
this
look
like?
This
is
very
cartoonish
and
I
apologize.
I
want
to
draw
your
attention
to
the
right-hand
corner,
so
this
is
the
web
of
design.
We
envision
words.
We
see
in
a
very
highly
collaborative
new
design
community,
both
within
Lockheed
with
our
partners
and
with
a
new
service
echo
system
of
design
tools,
validation
tools,
click
turn,
sort
of
simulations,
allowing
us
to
do
quick
turn
virtual
test
and
feedback
to
our
design,
our
design
community.
B
That
should
allow
much
more
rapid
networking
and
distribution
of
test
patterns
again
out
to
a
envisioned
service
network
of
service
providers
to
do
first,
first,
article
of
printing
or
prototyping
and
testing.
Once
we
get
through
that
process,
we
can
go
into
a
more
secure
network
where
we
can
either
job
out
components
of
the
of
the
subsystem
to
to
trusted
partners
or
send
the
prince
Gretchen's
directly
out
to
a
Forward
Operating
Base.
B
So
what
is
enmity
I
facilitate?
We
see
the
foundational
framework
being
developed
by
MGI
is
developing,
enabling
edited
manufacturing
software
new
computational
tools,
new
analytics
to
radically
reduce
the
cost
new
design
cycle
to
get
us
to
shrink
that
tenure
timeline
that
the
it's
so
typical
for
the
DoD
rapid.
What's
so-called
composable
design,
automation,
optimization
what
we're
looking
for
new
framework,
we
really
need
help
from
the
computer
science
and
data
science.
Community.
I
think
this
is
a
great
idea
to
have
to
host
this
this
workshop
here
in
the
computer
science
building.
B
We
really
need
help
in
how
we,
how
we
frame
design
workflows
to
think
about
harken
back
to
the
days
of
how
the
internet
developed
the
internet
had
its
single
greatest
impact.
After
workflow
software
came
on
the
market,
simple
tools
that
we
overall
familiar
with
today,
like
Word
and
Excel,
that's
one!
That's
one
of
the
real
payoff
at
the
end
that
occurred.
I.
Think
the
same
thing
will
be
true
in
10
to
15
years
in
in
in
this
world,
as
new
software
tools
and
frameworks
evolved
and
become
usable,
as
at
Bryce
pointed
out.
B
B
Again
in
this
idea
of
a
new
service
sec
of
system
building
up
or
evolving,
we
see
we
envision.
You
know
the
possibility
of
new
business
models
for
service
providers
to
provide
virtual
validation
tools
and
and
actually
running
them
on
a
rapid
turn
basis
and,
finally,
a
human
machine
interfaces.
This
is
another
big
role
for
data
science,
on
machine
learning
and
artificial
intelligence.
How
the
experts
designer
materials
engineer
interacts
with
our
systems,
our
new
software
systems?
That's
a
that's
a
huge
challenge
in
the
world
of
human-machine
teaming
in
autonomy.
That's
that's!
B
B
You
know
that
the
icme
framework
is
he's
going
to
help
streamline
workflows,
to
streamline
data
flows.
There's
always
going
to
be
a
need
for
experts
on
the
loop
in
the
so
called
doodle-loop
cycle,
observe
orient,
decide,
act,
kind
of
a
cycle
enabling
concurrent
materials
and
the
product
design
in
them
in
the
design
cycle
where
the
critical
technology
needs,
we
need
fast
cell,
so
called
design,
decomposition
tools
and
optimization.
What
do
I
mean
by
designed
to
composition
instead
of
the
Pentagon
or
the
Air
Force
coming
to
its
industry
partners
are
saying:
I
need
this.
B
Generative
design
computational
tools
that
allow
us
to
build
from
the
ground
up,
rather
than
presupposing
what
that
design
should
look
like
robots
and
virtual
rapid
validation
of
verification.
That's
going
to
be
crucial
in
the
world
of
additive
manufacturing,
error
assessment
and
remediation
all
points
across
the
digital
thread.
The
ability
to
interrogate
parts
ads
are
being
built,
digitally
render
them
and
assess
where
the
errors
are
and
do
on.
The
fly
error.
Correction.
B
Well,
while
we're
building
a
new
ecosystem
for
web
based
platforms,
environments,
interoperability,
protocols,
machines
talking
to
computers,
designers,
collaboration
tools
as
design
libraries
are
going
to
be
huge
enablers
for
additives
and
it
finally
computer-aided
new
CAD
tools
for
added
manufacturing,
their
woefully
inadequate.
Today
for
for
additive
manufacturing
they're,
all
the
tools
that
are
on
the
market,
for
the
most
part,
were
built
for
subtractive
manufacturing
processes.
B
There's
a
big
role
for
materials
data
in
intersection
in
the
interceptions
design
analysis,
the
data
science
need
for
for
additive,
design,
abstraction
and
optimization.
This
concludes
techniques
like
heterogeneous
representation.
These
are
mathematical
representations
of
structure,
form,
multiscale
properties,
multiple,
multiple
materials.
B
We
need
multiscale
physics,
geometry
and
architecture,
optimization
and
compensation
all
throughout
the
process
for
error
and
uncertainty,
a
new
virtual
validation
of
verification
tool,
selection
of
the
ability
to
decide
in
the
universe
of
available
simulation
tools
which
are
the
most
appropriate
for
the
design
problem
which
one,
how
do
I
string,
those
together
into
a
a
workable
software
framework,
the
Gold's
new
gold
standard
data
set
for
validating
materials
in
additive
manufacturing.
We
follow
the
microstructures
from
Pratt
Whitney
on
what
our
micro
structures
look
like
when
we
put
them
through
processes
like
selected
latest
laser
centering.
B
They
are
not
the
same
material,
these
new
v,
V
and
B
requirements,
new
and
corrective
feedback
to
the
design
as
a
result
of
rapid
turn
simulation
and
then
finally,
the
human
machine
interface,
conceptual
design,
assistance
to
the
expert
being
able
to
interface
all
throughout
the
design,
optimization
and
then.
Finally,
the
final
design
outcome
assessment
isn't
real?
We're
always
going
to
need
an
expert
to
lay
eyes
on
our
machine
driven
solutions
to
make
sure
it
actually
makes
sense.
B
Word
about
informatics
again,
just
another
picture
of
our
view
of
informatics.
Basically,
computational
approaches
to
interpret
materials,
science
and
engineering
data.
These
are
data-driven
modeling
top-down
modeling
approaches
relies
on
the
development
of
meaningful
multiscale
process
and
structure
descriptors,
so
we're
not
going
from
process
directly
to
properties
which,
frankly,
is
in
common
practice
in
the
aerospace
industry.
Today,
we'd
like
to
get
to
adoption
of
these
data
science
and
informatics
tools
to
help
us
do
more
statistical
certainty,
based
connection
of
process
to
structure
the
properties.
B
We
have
been
at
work
developing
and
implementing
in
health
informatics
tool
within
Lockheed
I
think
this
is
complementary
to
sit
trained
and
we
call
it
the
materials,
data,
mining,
modeling
and
management
tools.
That's
a
mouthful
that
allows
us
to
it's
a
very
adaptable
tool
for
very
different
materials
that
may
very
different
manufacturing
process,
domains
for
looking
for
trends
and
data
sets
for
product
performance.
B
We
have
a
number
of
analysis,
algorithms,
the
run
a
series
or
parallel
for
prediction,
results
based
on
on
various
inputs,
as
well
as
providing
guidance,
really
rapid
guidance
to
the
experimentalists
and
significantly,
and
it
significantly
informatics
provides
ways
to
visualize
the
data
I
wish
I'd
had
this
tool
available
to
me.
15
years
ago,
when
I
was
looking
at
massive
data
sets
for
my
I'm
PhD
I
think
it
would
have
really
provided
insights
that
I
might
not
have
ever
seen.
B
What
are
we
needing
they
in
the
in
the
workforce?
So
I
these?
This
is
you
know
a
slice
of
what
I
would
I
Percy
is
being
being
highly
valuable
tools
for
the
free
additive
manufacturing
ecosystem,
so
design,
abstraction,
optimization,
multifunctional
conceptual
design
and
autumn?
Is
it
automation
tools?
B
This
is
going
to
be
heavily
drawn
upon
from
the
mechanical
engineering,
Electrical
Engineering
communities,
as
well
as
the
computer
science
and
data
science
communities,
multiphysics,
multi-skilled,
topology,
optimization
that
will
play
a
crucial
role,
role
in
design,
optimization
validation
and
verification,
and
the
ability
to
include
uncertainties
of
quantification
and
in
our
models
in
our
design
and
we're
still
going
to
need
significant
expertise
in
structures
and
systems,
complex
structures
and
systems.
Certification,
human
machine
interface
need
to
draw
upon
the
organizational
in
industrial
psychology
communities
as
well
as
machine
learning
and
Design.
B
Systems
models
future
needs
on
the
engineering
side,
I'm
not
going
to
go
through
all
these,
but
much
the
way
I
CME
is
advancing.
Our
government
partners
are
advancing
I,
say
made
through
foundational
engineering
problems.
I
think
the
same
can
be
said
for
4mg
I,
realistically
complex
challenge,
problems
to
advance
the
field,
to
advance,
to
save
the
art
indexing
and
describing
the
vast
range
of
different
additive
manufacturing
machine
types,
their
interactions
with
materials,
interoperability,
protocols
and
constraints.
B
There's
a
lot
of
work
going
on
today
in
developing
process
simulations
for
additive
manufacturing.
There's
not
enough.
You
might
be
my
view
of
material
interactions
being
included
in
their
simulations
experimental
data
crossing
the
various
families
of
abetted,
manufacturing
technologies
and
materials.
The
citrine
database
could
be
a
suitable
platform
for
for
capturing
that
the
best
sets
of
data
that
are
being
generated
in
various
test
beds,
like
the
National
Lab
research,
advanced
methods,
I'm,
repeating
myself
and
designed
composition,
I
think
there's
a
lot
of
work
going
on
in
the
geometric
math
academic
community.
B
There
I
think
we
need
to
pay
attention
to
that.
There's
a
lots
of
great
science
and
tool
sets
coming
out
of
that
community.
Adaptable
multiscale
design,
optimization
uncertainty,
signature
determination
technique,
so
it's
not
just
done
I.
Think
there's
a
role
for
uncertainty
is
helping
us
characterize
our
processes,
you
think
of
it
as
a
tool,
not
just
something
we're
trying
to
constantly
reduce
using
it.
The
uncertainty
signature
as
a
as
a
as
a
diagnostic
for
how
our
materials
are
interacting
with
complex
geometries
in
additive
manufacturing.
B
Culture
shift
so
within
within
my
industry.
I
think
we're
going
to
need
to
see
the
development
of
requirements
becoming
a
customer
company
collaboration,
but
I
mean
our
designers
need
to
be
working
closely
with
our
customers
breaking
down
this
idea
of
static
materials,
spec
sheets.
Let
you
sell
in
the
pratt
whitney
presentation.
B
We
need
better
descriptors
of
our
materials
and
that
confection
arise
from
informatics
tools.
Requirements
themselves
in
an
embedded,
multi
functional
designs
need
to
be
traded
in
optimized
so
that
the
solution
may
be
a
trade-off
between
requirements
as
we
go
through
the
optimization.
We
need
new
methods
for
design
representations,
a
new
common
data
format
between
design
and
analysis.
Today,
they're
hideously
and
compatible
the
design
CAD
data
structures
are
frankly
leaky.
They
have
imperfect
interfaces
at
boundaries
if
we
can
find
some
other
way
to
represent
geometry.
B
That's
suitable
for
for
finite
element,
analysis
that
would
greatly
speed
up.
We
have
some
efforts
in
aerospace
to
take
months
or
up
to
a
year
just
to
do
those
types
of
transitions
right,
so
radically
new
analysis
methods
like
b-spline,
T,
splines
I,
think
we
need
to
be
open-minded
about
some
of
these
newer
analysis
tools.
Data
sharing
I
think
could
be
promoted
by
a
sprouting
up
of
new
service
models
for
design
collaboration,
optimization
of
validation,
so
I
vision,
a
future
collaborative
or
commercial
service.
Enterprise
like
I,
said
that
in
quotes
your
lettuce
thousand
citrine's
bloom.
B
That
model
could
work
quite
well
for
a
number
of
other
types
of
services.
I
think
15
is
really
on
the
front
line
for
for
that
type
of
model,
and
that's
I
think
they're
going
to
be
a
great
test
case
for
this
material
science
and
engineers
need
to
begin
to
think
of
themselves
as
data
scientists
and
an
architect's
embracing
mathematical,
optimization
to
apolog--,
optimization
statistical
representation.
B
Think
of
uncertainty
as
your
friend.
It's
a
way
to
better
understand
your
performance
if
its
way
to
better
that
provides
more
robust
design,
and
so
it's
a
way
to
help.
You
better
understand
the
the
interactions
between
the
material
and
these
very
very
complex
additive
processes
that
that
create
defects.
B
B
There
really
needs
to
be
much
greater
engagement,
like
the
type
of
being
fostered
here
in
the
big
data
hub
between
the
materials
in
manufacturing
communities
in
the
data
science,
communities.
I
live
in
the
world
of
people
that
do
IT
and
data
science
for
living
I'm
in
the
minority,
I'm
completely
surrounded
by
it.
That
community
has
a
lot
to
offer.
They
are
there
they're
very
creative,
they're,
very
innovative
I
think
we're
going
to
see
a
lot
of
really
exciting
creative
ideas
coming
from
this
computer
and
data
science
community,
let's
go
ahead.
Thank
you.