This paper explains and gives examples of how AI can be used to efficiently explore the highly multi-dimensional challenge of developing new catalysts and how hierarchical, graphical AI models can be used to model complex interactions and compensate for scarce data through transfer learning.
Learn how Machine Learning for Materials and typical AI applications are different. See how Citrine has overcome these difficulties and why off-the-shelf open source AI will require a lot of tailoring to make it work in this space.
Formulations development has unique challenges associated with the vast spectrum of possible ingredient combinations and the complex rules that govern their mixing. Learn how we address these challenges with formulations-specific AI and data management approaches.
Learn how data can be used to accelerate materials development. Read about the principles of good data management. See how Citrine's Platform supports data management in a way that is tailored for materials and chemicals industries.
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