Building the materials foundation for AI
AI’s expansion is becoming a materials challenge: as computing pushes into new territory, the materials behind that infrastructure are as crucial as the algorithms running on it, and semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability. Those limits create demand for materials that can do more at once, while AI also gives materials scientists new ways to search the enormous universe of possible molecules and accelerate development. Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, says AI is now, from a material standpoint, pushing semiconductors and data centers to their physical limits.
As requirements accumulate—including high temperature, purity, electrical performance, chemical resistance, plasma resistance, and long-term stability—materials move toward what Finelli calls the “top of the pyramid.” He contends that advanced materials are not just supporting AI innovation but are increasingly defining what is possible. Syensqo is developing materials for high-voltage data center architectures, advanced sealing materials for semiconductor manufacturing, and thermal-management solutions including fluids for direct immersion cooling. Some of those innovations can cross industry boundaries: materials developed for electric vehicles can help address the higher voltage and energy-density demands emerging in data centers.
The definition of performance is also changing, with more customers expecting materials to meet technical requirements while reducing environmental impact. Finelli says Syensqo’s goal is to remove the trade-off between performance and sustainability, which means considering sustainability at the beginning of the research process instead of treating it as an additional requirement once a material has been developed.
AI is changing how materials are discovered, too. Syensqo is using AI agents to digitally synthesize millions of potential molecular combinations, predict their performance and sustainability characteristics, and narrow them to a much smaller group for laboratory testing. Finelli says the result is the ability to go “broader, deeper, and faster” while giving scientists more time to solve complex engineering problems. Looking ahead, he sees a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery—a feedback loop that could create an accelerated materials innovation cycle and expand what future technologies can achieve.