About the Webinar
Recorded: Tue, 5/19/26
Reducing both raw materials and processing costs — such as curing times and temperatures — remains a key challenge across material classes. For polyurethane (PU) foams, this challenge is compounded by the need to maintain critical mechanical properties and meet stringent flammability regulations. In this webinar, we will walk through a case study demonstrating how AI can simultaneously optimize for cost, mechanical performance, and flame retardancy in PU foams, by integrating domain knowledge with AI-driven experimentation.
We will also talk about a successful project where Huntsman Building Solutions used the Citrine Platform to predict performance in large-scale fire tests, significantly reducing development time frames.
Finally, we will conclude with practical guidance on how to accelerate early wins in AI materials projects.
Who should attend:
- Product development leaders who want to derisk qualification tests
- Product developers keen to understand how AI can help them understand their chemistry better
- Business leaders who need to cut ingredient costs without harming product performance
Why should you attend:
- Understand how easy AI is to use and how little data you need to start with
- See how AI can be used in practice to reduce costs while remaining compliant
- Learn how product experts can learn from AI, not be replaced by it
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