Advancing next-gen AI with materials science innovation

The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and higher reliability. Every increase in computing performance increases the physical demands placed on the systems that make and run AI. Delivering these gains depends not only on advances in chip design and system architecture, but on advances in the materials that enable them to perform under extreme conditions. As AI continues to push the physical limits of semiconductors and data center infrastructure, advanced materials are no longer simply supporting innovation in this area; they are defining the limits of what is possible. Performance first Advanced materials exist to solve performance challenges. As AI raises the bar, these challenges are becoming more demanding. Manufacturing a semiconductor chip today requires thousands of tightly controlled process steps, with almost no room for error. Tiny variations in temperature or chemical instability can create defects that reduce yield and drive up manufacturing costs. With every new generation of semiconductor chips, manufacturers seek advanced materials that can deliver greater purity, higher chemical and plasma resistance, and better stability under increasingly harsh operating conditions. These are familiar engineering challenges being pushed to new extremes. And it’s here that materials innovation makes the difference with continuous advances in polymers, elastomers, specialty fluids, and other advanced materials that make each new generation of technology possible. For materials companies, it’s not about reinventing semiconductor manufacturing but about ensuring the materials supporting the industry continue to evolve alongside it. This same principle applies beyond the semiconductor fabrication floor. As AI workloads become more demanding, the physical infrastructure that powers them is evolving rapidly. Increasing computing density is transforming data center design, driving the need for more sophisticated thermal management, higher-voltage power architectures, increased data storage, and faster, more reliable data transmission. Every part of the system is under greater pressure, from cooling and power management to critical electronic components, such as connectors, capacitors, and hard disk drives. At Syensqo, we’re building on our expertise in electronic and electrical components, along with insights from other markets, to meet these emerging needs. For example, as data centers shift to higher-voltage architectures and greater power density, many of the materials challenges we face closely mirror those of electric vehicles. Fluid-circulation know-how from semiconductor and automotive coolant systems, for instance, can be adapted to direct liquid-cooling designs for AI servers. By transferring knowledge across markets, we can accelerate new power and thermal management solutions while supporting the reliability required by next-generation AI infrastructure. Whether we’re talking about semiconductor fabrication or hyperscale server farms, the challenge for materials science companies is the same: enabling greater performance without compromising reliability. A new definition of what performance means While performance remains the first priority, the way performance is defined is changing. In addition to meeting the increasingly demanding technical requirements of next-generation semiconductors and data centers, there is now an expectation that these materials are developed and manufactured more responsibly. Perfluoroelastomers, for example, are used to seal semiconductor manufacturing equipment. These materials operate under extreme temperatures, aggressive plasma, and highly reactive chemicals. To make the process more sustainable, at Syensqo, our next generation of perfluoroelastomers use a fluorosurfactant-free manufacturing process. Our goal was to make a better-performing material, produced in a better way, ensuring manufacturers no longer have to choose between higher performance and a more responsible way of producing the materials that enable it. This approach reflects a broader reality across the industry. New materials aren’t adopted simply because they are new. Qualification can take years, and manufacturers only make changes when a material solves a genuine engineering challenge or enables new technology. Performance remains the price of entry. The difference today is that the definition of performance has expanded. Success increasingly depends on delivering technical excellence through more responsible manufacturing from the outset. Accelerating the pace of discovery As the performance bar rises, the way we innovate must evolve with it. Developing advanced materials has traditionally involved a lengthy process of hypothesis, synthesis, testing, and iteration. While this process remains unchanged, new digital tools are helping researchers move through these cycles faster. By helping researchers identify the most promising candidates earlier, AI can reduce the number of physical experiments required and accelerate the earliest stages of materials discovery. AI isn’t replacing scientific expertise. It’s helping scientists apply that expertise more effectively, allowing them to spend less time searching for answers and more time solving the industry’s toughest challenges. At Syensqo, we’re putting this approach into practice through use of several AI tools, including the Microsoft Discovery platform, which are helping researchers identify and evaluate promising molecular candidates for next-generation heat transfer fluids, used in semiconductor manufacturing and data centers. AI helps our researchers rapidly identify and evaluate promising molecular candidates based on the properties they need to achieve. This allows us to focus laboratory work where it has the greatest potential to deliver results, accelerating discovery and reducing the time needed to turn promising materials into solutions customers can qualify and deploy. The journey from laboratory discovery to a qualified material will always require scientific expertise, rigorous testing, and close collaboration with customers. But by accelerating the earliest stages of discovery, AI can help materials innovation keep pace with the evolving needs of industries such as semiconductors, electronics, and data centers. Progress is earned The future of artificial intelligence will depend on better algorithms, more powerful chips, and larger computing infrastructure. But sustaining that progress will also require advances in the materials that make those technologies possible. Whether in semiconductor manufacturing or AI infrastructure, progress is earned. Every new generation of technologies raises the bar, and every new material must prove it can deliver the performance, reliability, and efficiency needed before it earns its place. For materials companies, that remains both the challenge and the opportunity. This content was produced by Syensqo. It was not written by MIT Technology Review’s editorial staff.