Cutting-edge machine learning techniques, tailored specifically for materials and chemicals
What is it used for?
Citrine's Approach to AI
An easily interpretable graphical model
WORKING WITH SMALL DATA SETS
The Citrine Platform uses algorithms that work well on the small, sparse data sets usually available in materials companies. Citrine has tackled projects with fewer than 30 initial data points!
LEVERAGING DOMAIN KNOWLEDGE
A graphical model enables researchers to see how components fit together and facilitates the integration of expert domain knowledge. In the example here, a known relationship between binding energy and selectivity is leveraged. The machine learning models are used to fill in gaps in knowledge, rather than to relearn the basics.
Once built, each component can be used as part of other machine learning workflows, contributing to a library of codified domain knowledge that researchers across the team can use.
SEQUENTIAL LEARNING WORKFLOWS
The next generation of design of experiments is facilitated by the Citrine Platform. The iterative processes of Sequential Learning are coupled with systematic uncertainty calculations to enable efficient, systematic exploration of parameter spaces.