Building the materials foundation for AI
The AI boom is becoming a materials challenge. As AI pushes computing into new territory, the materials behind that infrastructure are becoming just as crucial as the algorithms running on it.
The rapid growth of artificial intelligence (AI) is pushing the physical limits of existing materials used in semiconductors and data centers. These materials must now meet increasingly complex demands such as high temperature resistance, electrical efficiency, chemical stability, and long-term reliability. Traditional materials are often insufficient, requiring the development of advanced, high-performance materials to support AI infrastructure. Mike Finelli, Chief Technology and Innovation Officer at Syensqo, describes this challenge using a pyramid metaphor: commodity materials form the base, while specialized, high-performance materials occupy the top. AI’s advancement is accelerating the need for materials at the top of this pyramid, as it introduces more stringent requirements like higher purity, plasma resistance, and energy efficiency. Finelli emphasizes that advanced materials are no longer just supporting AI but are now defining what future technological possibilities can be achieved.
Syensqo is a global leader in specialty materials, enabling innovation across industries such as electronics, healthcare, aerospace, and automotive. The company develops high-performance materials that enhance reliability, performance, and sustainability. Notably, 20% of Syensqo’s annual revenue comes from products launched in the last five years, reflecting its strong focus on innovation. Syensqo’s materials are integral to advanced semiconductor manufacturing, thermal management, and data center infrastructure. For example, the company produces polymers and fluids used in semiconductor fabrication, high-voltage data center architectures, and sealing materials for semiconductor tools. These materials must withstand extreme conditions, such as high temperatures, aggressive chemicals, and plasma environments, ensuring the reliable production of next-generation chips.
Syensqo leverages expertise from one industry to solve challenges in another, particularly between the automotive and data center sectors. For instance, materials developed for electric vehicles (EVs) are being adapted for data centers. EVs require materials that can handle high energy densities and temperatures, such as insulating polymers in battery bus bars. Similarly, data centers moving toward high-voltage architectures face comparable thermal and electrical challenges. Syensqo’s materials for direct immersion cooling—where semiconductors are submerged in a liquid to improve efficiency—are another example of cross-industry innovation. Additionally, the company’s high-performance binders for lithium-ion batteries in EVs are being applied to energy storage systems in data centers, which are increasingly relying on renewables for backup power.
AI is not only driving the demand for advanced materials but also transforming how these materials are discovered. Syensqo uses AI agents to digitally synthesize millions of potential molecular combinations, predicting their performance and sustainability characteristics. This process narrows down the options to a smaller group for laboratory testing, significantly speeding up research and development. By leveraging AI, Syensqo can explore a broader range of materials more efficiently, allowing scientists to focus on solving complex engineering problems. This approach enables the company to develop materials that meet both technical and environmental requirements faster than traditional methods. Finelli highlights this as a key factor in enabling a cycle of innovation where AI improves materials, and advanced materials, in turn, enhance AI capabilities.
Modern customers expect materials to deliver high performance while also being environmentally responsible. Syensqo addresses this by integrating sustainability into the research process from the outset, rather than treating it as an afterthought. The company uses a tool called the Sustainable Portfolio Management (SPM) matrix to assess whether a product will improve social and environmental performance while reducing its production impact. This approach ensures that materials meet both technical and sustainability goals. For example, Syensqo’s high-voltage data center materials aim to improve energy efficiency and lower environmental footprints, aligning with the broader industry trend of reducing the carbon footprint of AI infrastructure. The goal is to eliminate the trade-off between performance and sustainability, creating materials that are both high-performing and responsible.
Finelli envisions a reinforcing cycle where AI and advanced materials drive each other’s progress. As AI pushes the limits of materials science, new materials enable further advancements in AI infrastructure. This feedback loop could lead to breakthroughs in areas like thermal management, energy efficiency, and reliability, expanding the possibilities for future technologies. Syensqo’s work in high-performance polymers, specialty fluids, and sealing materials positions it to play a critical role in this cycle. The company’s focus on innovation, sustainability, and cross-industry solutions ensures it remains at the forefront of enabling technologies that will shape the future. This dynamic relationship between AI and materials science underscores the importance of continuous innovation in both fields.
