And we really cannot afford to explore the spaces through very careful experiments.
We have to learn how to skim through this space fast,
find locations of interest and then pay detailed attention to particular locations
where there is valuable information.
Yet another key to what we want to do is uncertainty quantification.
Given that we are going to use high throughput protocols and
this are not going to be exact in what we're looking for.
We need to quantify uncertainty and
use that information in decision in making this.
And in doing that, we want to employ data driven processes.
Because we believe data driven processes lead to best decisions or
objective decisions, and are likely to save us time and money.
Another key to accomplishing what we envision are the collaborations.
As we discussed before the meticulous development activity
leading to improved product design.
A very multidisciplinary,
it needs a lot of different expertise from different stakeholders.
And we need to learn how to collaborate faster and better.
And this is going to be produced by cyber infrastructure and
we will talk about that later in this class.
A final key to accelerated materials innovation comes from
digital recording of workflows.
One of the important aspects of work we are discussing so
far is that the workflows are fairly complicated.
They involve a lot of different physics, a lot of different length scales,
lot of different pools, lot of different expertise.
We need to actually understand and record which workflows work better.
We not only need to understand, learn from our successes.
But we also need to learn from our failures.
We will be able to establish the best workflows.
In summary, Materials Data Sciences and
Informatics is going to help us With all these items that are listed here.
And we're going to learn exactly how we're going to do that in the coming lessons.
So, in summary for this lesson.
We learned that structure plays a central role in establishing PSP linkages.
We need to quantify structure in a statistical framework.
We absolutely need a low-dimensional representation for
it to be practically useful in materials development efforts.
And the quantification of
structure is foundational to Materials Data Sciences and Informatics Approaches.
Thank you.
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