Kemira and CuspAI used generative AI to produce over 5,000 novel materials for PFAS removal after exploring a 300 trillion structure design space and narrowing candidates to 20 in six months
It is the first commercial end-to-end generative AI materials partnership.
#56Dan Roy@ROYDANROYAwesome.
Max Welling@wellingmaxToday @cusp_ai and @KemiraGroup announce a milestone in AI-driven materials discovery. We have 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.
The brief from Kemira was understandably demanding.
Max Welling@wellingmaxToday @cusp_ai and @KemiraGroup announce a milestone in AI-driven materials discovery. We have used generative AI to design new materials targeting PFAS removal from drinking and process water at trace concentrations.
@KemiraGroup 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.
Max Welling@wellingmaxPFAS are a hard and important class of problems: persistent synthetic chemicals, present in water systems worldwide, and subject to tightening regulation. The brief from Kemira was understandably demanding.
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.
Max Welling@wellingmaxOur 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.
Our 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.
Max Welling@wellingmax@KemiraGroup 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.
Max Welling@wellingmaxThe candidates are now advancing to further development and testing. I am proud of the @cusp_ai team and excited by what this says about the future of materials discovery!
The candidates are now advancing to further development and testing.
I am proud of the @cusp_ai team and excited by what this says about the future of materials discovery!
Max Welling@wellingmaxThis 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.
#120Taco Cohen@TACOCOHENThere’s been a lot of talk about AI revolutionizing science and human health, but I feel like often there’s a gap between the narrative (often by consumer AI companies) and the actual results.
Now it’s actually starting to happen: Max’s company CuspAI is making serious progress towards AI-discovered materials for PFAS removal.
Max Welling@wellingmaxhttps://www.kemira.com/news-and-stories/newsroom/releases/new-ai-designed-materials-show-promising-potential-to-remove-forever-chemicals-from-drinking-water-in-industry-first-breakthrough/