Building the materials foundation for AI
2026-09-16 · MIT Technology Review
Building the materials foundation for AI
The AI boom is transforming computing, making the materials behind the infrastructure as crucial as the algorithms running on it. Semiconductors and data centers are approaching physical limits regarding performance, thermal management, electrical efficiency, and reliability. This creates a demand for materials capable of doing more simultaneously. According to Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, AI is pushing these systems to their absolute physical boundaries.
Materials Challenges and the "Top of the Pyramid"
As requirements accumulate—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 increasingly defining what is technologically possible, rather than merely supporting existing innovations.
This challenge spans the entire infrastructure powering the AI surge. Syensqo is actively developing several key solutions:
- Materials for high-voltage data center architectures: Addressing higher voltage and energy-density demands.
- Advanced sealing materials for semiconductor manufacturing: Withstanding rigorous fabrication environments.
- Thermal-management solutions: Including fluids for direct immersion cooling.
Furthermore, some of these innovations cross industry boundaries. For instance, materials originally developed for electric vehicles can be adapted to meet the emerging high-voltage and energy-density requirements of data centers.
Merging Performance with Sustainability
The definition of performance is also evolving. Customers increasingly expect materials to meet technical requirements while simultaneously reducing environmental impact. Finelli emphasizes that Syensqo's goal is to eliminate the trade-off between performance and sustainability. This involves integrating sustainability considerations at the very beginning of the research process, rather than treating it as an afterthought.
AI Accelerating Materials Discovery
AI is not just creating challenges; it is also revolutionizing how materials are discovered. Syensqo utilizes AI agents to:
- Digitally synthesize millions of potential molecular combinations.
- Predict their performance and sustainability characteristics.
- Narrow the candidates down to a much smaller group for laboratory testing.
This approach allows the company to go "broader, deeper, and faster," giving scientists more time to focus on solving complex engineering problems.
A Reinforcing Cycle of Innovation
Looking ahead, Finelli envisions a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery. This feedback loop could create a continuous cycle of innovation, expanding the boundaries of future technologies.
"You end up in this accelerated materials, innovative cycle of materials innovation," says Finelli. "That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future."