Designing Materials: Why AI Is Becoming a Strategic Necessity

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Chemicals companies are under pressure from several directions at once: sustainability mandates that demand new formulations, supply chains that need to be reshored or diversified, and R&D budgets that are expected to deliver faster without growing larger. 

Trial-and-error development, the industry‘s default for decades, is increasingly seen as too slow and too expensive to meet these demands alone. AI-driven materials informatics platforms have emerged as a possible solution, helping scientists mine decades of experimental data, run virtual experiments, and narrow the search space for new materials before a single sample is made in the lab. Citrine Informatics, founded in 2013, was among the first companies to build a commercial software platform around this idea, and has since worked with manufacturers across chemicals, batteries, semiconductors, and industrial materials. CHEManager‘s Christene Smith spoke with Greg Mulholland, CEO and co-founder of Citrine Informatics, about how the market for materials informatics has matured, what still holds companies back from adopting it, and where he sees the industry heading next.

CHEManager: Citrine has been arguing that AI belongs in materials R&D since 2013, well before the current wave of AI enthusiasm. Now that every software vendor claims an AI story, how do you convince a skeptical R&D leader that this is substantively different from the hype cycle around it?