Advanced materials become a bottleneck for next-generation AI

AI development depends on more than algorithms, chips, and data centers. Rising demand for processing power, memory, energy efficiency, and reliability is increasing pressure on the materials used in semiconductor manufacturing and AI infrastructure. Chip production requires tightly controlled processes, where small changes in temperature or chemical stability can create defects, reduce yield, and raise costs.

Advanced polymers, elastomers, specialty fluids, and related materials are being pushed to handle harsher environments, including aggressive plasma, reactive chemicals, higher-voltage power systems, and denser computing layouts. Syensqo says expertise from semiconductor and automotive coolant systems can be applied to direct liquid-cooling designs for AI servers, while materials used in connectors, capacitors, hard disk drives, and power systems must also evolve.

Performance requirements are also expanding to include more responsible production. Syensqo points to next-generation perfluoroelastomers for semiconductor equipment seals that use a fluorosurfactant-free manufacturing process, aiming to improve performance without forcing manufacturers to compromise on production practices.

AI is also becoming a tool for materials discovery. Syensqo is using systems including the Microsoft Discovery platform to identify and evaluate molecular candidates for heat transfer fluids used in semiconductor manufacturing and data centers, helping researchers narrow laboratory work to the most promising options while preserving the need for testing, expertise, and customer qualification.