Today Cusp AI and Kemira announced a milestone in AI-driven materials discovery.

They used generative AI to design new materials targeting PFAS removal from drinking and process water at trace concentrations.

PFAS are a hard and important class of problems: persistent synthetic chemicals, present in water systems worldwide, and subject to tightening regulation.

Kemira defined requirements: target specific PFAS molecules, operate at sub-parts-per-billion concentrations, and use chemistry that is stable, sustainable, synthesizable and cost-effective.

That matters. AI discovery has to meet physical and industrial constraints.

A platform explored a design space of ~300 trillion possible MOF structures and generated more than 5,000 novel material designs with property data for GenX, PFBS and PFOS.

These were narrowed to around 20 priority candidates.

This is a shift from AI as a screening tool to AI as a generative design system: creating new structures from scratch, then evaluating them against real requirements.

The project reached this stage in six months, not years.

The candidates are now advancing to further development and testing.