Material discovery and humankind
go hand in hand.
They are driving much of the progress
today in society.
It takes approximately 10 years
and 10 million dollars
from the design concept
to the market
for a single material.
And this is a time
that we can not afford.
Especially in situations
where we have threatening diseases
that are affecting
the entire humanity.
Moreover, the way
we are making molecules
didn't really change
in the last 200 years.
So the very first question
that we asked is
how to accelerate
the material design using AI.
You need a lot of expertise
and experience
and these are two characteristics
that are difficult to scale
when you want to reduce
the development time.
I remember well
we gathered together with the group
and we started exploring
a few opportunities.
And this was the precise moment
where RoboRXN was actually born.
RoboRXN is a pioneering project
that shows how the combination
of three technologies
cloud, AI and automation
can dramatically change
the way we work.
Imagine a chemist at home,
willing to make a molecule,
connecting to RoboRXN
through a web browser,
drawing the molecule
and having RoboRXN recommending
the optimal synthetic route
together with the starting
commercial material,
and finally RoboRXN
self-programming itself
to execute the process
in an autonomous laboratory.
One of the core ideas
of the project
is to treat organic chemistry
as a language.
You can basically represent
your atoms as letters,
molecules as words and
chemical reactions as sentences.
For example 
for chemical reaction prediction
we can cast the whole task
as a translation task
and go from one language
to another language.
It's important, every time
you're applying artificial intelligence
to a new domain like materials,
to start from data which is
embedding the knowledge of the domain.
So we needed to have data
that was representing
the entire knowledge
that humans have
in the context
of chemical synthesis.
Data is everything.
Data is the core of our model.
The ability for the robot 
to provide us
with new high-quality data
will allow us to build
even better models.
To train the models
we generated a data set
from published reactions,
which are available
in patents for instance.
The fact that we have
a cloud platform
that serves our AI models
and allow you to transmit
and translate recipes
to a robotic hardware
is an extremely scalable solution.
Because you can reach
any chemist around the world,
allow them to submit molecules
and then execute the recipes
wherever you want,
in any lab that is connected
to the cloud platform.
So with the RoboRXN technology
we are expecting
to accelerate profoundly
the way we do material discovery.
It's revolutionizing
an entire field,
bringing chemistry
from a traditional type of business
into a hightech business.
Automation itself
is relieving the chemist
from tedious tasks,
repetitive tasks
and leaves way more
time and space
for the design
and the innovation.
