Artificial Intelligence
Cutting-edge machine learning techniques, tailored specifically for materials and chemicals
What is it used for?
Optimize composition
and processing to hit
new performance specifications
Machine Learning Accelerates Materials Development
Learn how the Citrine Platform helped Panasonic develop new organic semiconductors with 25% higher hole mobility.
Quickly reformulate to reduce recipe costs AND improve supply chain resiliency
Reduced Costs and Increased Supply Resilience
Learn how Citrine’s AI Platform can be used to reduce costs and increase supply chain resilience in specialty chemicals.
Make
project-level
R&D
decisions
Data-Driven Research Strategy
Learn how data-driven assessments of research directions drive R&D strategy at a global glass manufacturer.
Citrine's Approach to AI
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. The machine learning models are used to fill in gaps in knowledge, rather than to relearn the basics.
REUSABLE COMPONENTS
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.