What We’ve Learned

Models, in isolation, generate no value. A model is an incredibly powerful tool—it combines expert knowledge with real-world data to extract insight, uncover hidden relationships, and communicate what truly matters across teams. But a powerful tool left idle on a desktop never helps anyone. In materials, chemistry, and product development, a mindset shift is required: away from perfecting models in isolation and toward using data-driven tools to design, test, and validate real materials that deliver real-world value.

We’ll explore smart data practices at three critical levels: organizational strategy, team management, and individual research projects. Whether you’re starting with minimal datasets or managing complex data infrastructure, these proven approaches will accelerate your AI transformation journey.

R&D leaders embarking on a digital transformation journey face a common dilemma: should you start with Electronic Lab Notebooks (ELN), a Laboratory Information Management System (LIMS), or a Materials Informatics platform – and in which order?

Packaging manufacturers face unprecedented challenges across multiple dimensions. The delicate balance between performance objectives, regulatory compliance, sustainability mandates, and supply chain volatility has created a complex landscape that traditional approaches struggle to navigate efficiently.

AI will transform R&D, but only if scientists can trust and adopt the tools built for them.

What sets Citrine apart from its competitors is its ability to deliver tangible business value swiftly, thanks to a unique blend of technological innovation and deep domain expertise.

ChatGPT has revolutionized AI-assisted tasks, but it’s not suited for everything—especially in materials and chemical product development.

In the fast-paced world of coatings, adhesives, and sealants (CAS), there’s a pressing need to accelerate research and development (R&D) and utilize better tools for finding substitute ingredients, optimizing multiple objectives, and transferring knowledge to new employees. AI Can help.

The product experts that use our AI don’t see the Citrine Platform as a black box, but as a flashlight. And that flashlight just got stronger.

Although many companies are already reaping the rewards of using AI, others are being held back by myths around the need for large datasets. While representative data can propel an AI project forward quickly, you don’t need to wait for large, structured datasets to start your AI journey.