Advancing next-gen AI with materials science innovation

以材料科学创新推动下一代人工智能发展

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. 关于人工智能的讨论往往集中在算法、计算能力，或是对新建半导体晶圆厂和超大规模数据中心的巨额投资上。然而，在每一项进步的背后，还有另一层使其成为可能的创新：先进材料。

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. 每一代人工智能技术都要求更高的处理能力、更大的内存、更高的能效和更强的可靠性。计算性能的每一次提升，都会增加对制造和运行人工智能系统的物理要求。实现这些进步不仅取决于芯片设计和系统架构的突破，更取决于那些使其能够在极端条件下运行的材料的进步。随着人工智能不断挑战半导体和数据中心基础设施的物理极限，先进材料已不再仅仅是这一领域的辅助创新，它们正在定义可能性的边界。

Performance first

性能至上

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. 先进材料的存在是为了解决性能挑战。随着人工智能不断提高门槛，这些挑战也变得愈发严苛。如今，制造一枚半导体芯片需要数千个严格控制的工艺步骤，几乎没有容错空间。温度的微小波动或化学不稳定性都可能产生缺陷，从而降低良率并推高制造成本。随着每一代半导体芯片的更迭，制造商都在寻求能够提供更高纯度、更强化学和等离子体耐受性，以及在日益严苛的操作条件下具备更好稳定性的先进材料。

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. 这些都是我们熟悉的工程挑战，但现在正被推向新的极端。正是在这里，材料创新发挥了关键作用——通过聚合物、弹性体、特种流体及其他先进材料的持续进步，使每一代新技术成为可能。对于材料公司而言，重点不在于重塑半导体制造工艺，而在于确保支撑该行业的材料能够与行业同步演进。

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. 同样的原则也适用于半导体制造车间之外。随着人工智能工作负载的需求日益增长，支撑它们的物理基础设施也在迅速演变。计算密度的增加正在改变数据中心的设计，推动了对更复杂的散热管理、更高电压的电力架构、更大的数据存储空间以及更快、更可靠的数据传输的需求。从冷却和电源管理到连接器、电容器和硬盘驱动器等关键电子元件，系统的每一个部分都承受着更大的压力。

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. 在 Syensqo，我们正依托在电子和电气元件领域的专业知识，结合来自其他市场的洞察，以满足这些新兴需求。例如，随着数据中心转向更高电压的架构和更高的功率密度，我们面临的许多材料挑战与电动汽车领域非常相似。例如，半导体和汽车冷却系统中的流体循环技术诀窍，可以应用于人工智能服务器的直接液冷设计。通过跨市场转移知识，我们能够加速开发新的电力和热管理解决方案，同时支持下一代人工智能基础设施所需的可靠性。无论是半导体制造还是超大规模服务器集群，材料科学公司面临的挑战都是一样的：在不牺牲可靠性的前提下实现更高的性能。

A new definition of what performance means

性能定义的新内涵

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. 虽然性能仍然是首要任务，但性能的定义方式正在发生变化。除了满足下一代半导体和数据中心日益严苛的技术要求外，人们现在还期望这些材料的开发和制造更加负责任。例如，全氟弹性体被用于密封半导体制造设备。这些材料在极端温度、强腐蚀性等离子体和高活性化学品环境下工作。为了使工艺更具可持续性，Syensqo 的下一代全氟弹性体采用了无氟表面活性剂的制造工艺。我们的目标是制造出性能更好、生产方式更优的材料，确保制造商不再需要在更高的性能与更负责任的生产方式之间做出取舍。

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. 这种方法反映了整个行业更广泛的现实。新材料的采用绝非仅仅因为它们是“新的”。认证过程可能需要数年时间，制造商只有在材料能够解决真正的工程挑战或实现新技术时才会做出改变。性能仍然是准入门槛。如今的不同之处在于，性能的定义已经扩展。成功越来越依赖于从一开始就通过更负责任的制造方式来实现卓越的技术表现。

Accelerating the pace of discovery

加速发现的步伐

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. 随着性能门槛的提高，我们的创新方式也必须随之演进。传统上，开发先进材料涉及假设、合成、测试和迭代的漫长过程。虽然这一过程本身没有改变，但新的数字工具正在帮助研究人员更快地完成这些周期。通过帮助研究人员更早地识别出最有希望的候选材料，人工智能可以减少所需的物理实验次数，并加速材料发现的最早期阶段。

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. 人工智能并不会取代科学专业知识。它是在帮助科学家更有效地应用这些专业知识，让他们减少寻找答案的时间，将更多精力投入到解决行业最棘手的挑战中。在 Syensqo，我们正通过使用多种人工智能工具（包括 Microsoft Discovery 平台）将这一方法付诸实践，这些工具正在帮助研究人员识别和评估用于半导体制造和数据中心的下一代传热流体的候选分子。