KEMI
Live News • May 24
Kemira Advances AI-Driven PFAS-Remediation Materials for Next-Generation Water Treatment Kemira Oyj and CuspAI report that their generative AI collaboration has produced new PFAS-remediation materials for water treatment, targeting harmful "forever chemicals."
The AI system evaluated roughly 300 trillion potential material structures and generated over 5,000 novel designs.
Around 20 priority material candidates are now moving into further development, marking what the partners describe as the first end-to-end commercial use of generative AI for PFAS-remediation material design.
The use of generative AI for PFAS-remediation materials highlights Kemira’s focus on advanced water-treatment solutions and may influence how its product pipeline evolves over time.
Investors may want to watch how many of the roughly 20 lead candidates progress toward commercial use, as well as the development timelines and regulatory considerations that emerge around PFAS treatment technologies. Announcement • May 23
Kemira Oyj and Cuspai Use Generative Ai to Design Novel Materials Targeting Pfas Removal At Trace Concentrations Kemira, a global leader in sustainable chemical solutions for water-intensive industries, and CuspAI, the frontier AI materials science company, announced that they have used generative AI to design new materials targeting the removal of PFAS, so-called 'forever chemicals,' from drinking and process water at trace concentrations, using chemistry that is stable, sustainable, and manufacturable. The materials discovery project explored a design space of approximately 300 trillion possible material structures and delivered over 5,000 novel material designs with full property data for three priority PFAS molecules: GenX, PFBS, and PFOS. This was narrowed to approximately 20 selected priority candidates now advancing to further development. The program reached this stage in six months. This is the first commercial partnership to apply generative AI end-to-end to the design of new materials for PFAS remediation. While academic groups and technology companies have explored AI for materials screening, no previous collaboration has taken a generative approach, designing entirely new structures from scratch against industrial performance criteria, and delivered candidates at this scale and speed. PFAS (per- and polyfluoroalkyl substances) are persistent synthetic chemicals found in drinking water worldwide. They are subject to tightening regulation, including US EPA maximum contaminant limits in parts per trillion scale (announced 2024) and the EU Drinking Water Directive. Today's leading remediation technology is granular activated carbon (GAC). Kemira, which is already active in the activated carbon regeneration market, initiated this collaboration to explore whether AI-driven design could open a path to more selective and even longer-lasting alternatives. Kemira defined the brief: discovery phase project of new materials that can remove specific PFAS molecules from water at sub-parts-per-billion concentrations, using chemistry that is water-stable, environmentally compatible, synthesizable, and cost-effective. CuspAI's platform designed entirely new metal-organic frameworks (MOFs) from scratch, searching approximately 300 trillion possible structures and delivering over 5,000 novel potential material designs with property data across all three target molecules, narrowed to selected priority candidates. The speed and scale of this discovery phase was unprecedented: record amount of structures was evaluated and it took 6 months to reach these results. The project also uncovered new functional group chemistries with potential for development into broader adsorption products. The project is now moving into its next phase of further development and testing, and further programs across additional material classes are being scoped under the partnership's framework agreement. PFAS (per- and polyfluoroalkyl substances) are a group of persistent synthetic chemicals that have been used in various industries since the 1940s. They are called 'forever chemicals' because they do not break down in the environment and can accumulate in the human body over time. Metal-organic frameworks (MOFs) are a class of nano-porous crystalline materials whose structure and chemistry can be precisely tuned for specific filtration and adsorption applications. The design space for possible MOFs is vast, estimated at hundreds of trillions of candidate structures, making AI-driven approaches essential for navigating it efficiently. 
