内容来源：https://www.technologyreview.com/2026/09/16/1144014/building-the-materials-foundation-for-ai/

内容总结：

AI浪潮正推动半导体与数据中心逼近物理极限，先进材料由此成为决定性能、能效与可靠性的关键要素。Syensqo首席技术与创新官兼北美区总裁迈克·菲内利指出，随着高温、高纯度、电气性能、耐化学性、耐等离子体及长期稳定性等要求不断叠加，材料正迈向“金字塔顶端”，先进材料已不再仅仅支撑AI创新，而是日益定义AI的可能性边界。

Syensqo正围绕AI基础设施开发多类材料方案，包括面向高压数据中心架构的材料、用于半导体制造的高性能密封材料，以及包括直接浸没式冷却液在内的热管理解决方案。部分创新还可跨行业迁移，例如为电动汽车开发的材料可帮助应对数据中心日益增长的高压与高能量密度需求。

性能的定义也在发生变化。越来越多客户期望材料在满足技术指标的同时降低环境影响。菲内利表示，公司的目标是消除性能与可持续性之间的取舍，在研发初期就将可持续性纳入考量。目前，Syensqo已有88%的产品组合属于可持续产品。

AI同样正在改变材料的发现方式。Syensqo与微软合作，利用AI智能体对数百万种潜在分子组合进行数字化合成，预测其性能与可持续性特征，并筛选出约百种候选分子进入实验室测试。菲内利称，这种方法使研发能够“更广、更深、更快”，同时让科学家将更多时间投入复杂工程问题的解决。

展望未来，菲内利认为存在一个自我强化的循环：AI帮助开发改进AI基础设施的材料，更好的AI又加速材料发现。这一反馈回路有望形成加速的材料创新周期，持续拓展未来技术的可能性。

中文翻译：

赞助内容

为人工智能构建材料基础

Syensqo首席技术与创新官兼北美区首席官迈克·菲内利表示，随着人工智能将半导体和数据中心推向新的物理极限，先进材料对性能、效率和可持续性正变得至关重要。

与Syensqo合作呈现

人工智能的热潮正演变为一场材料挑战。随着人工智能将计算推向新领域，支撑这一基础设施的材料正变得与运行其上的算法同等关键。半导体和数据中心正逼近性能、热管理、电气效率和可靠性方面的物理极限，这对能够同时满足多重需求的材料提出了新要求。与此同时，人工智能正为材料科学家提供新工具，帮助他们在浩瀚的可能分子空间中搜索，并加速解决方案的开发。

对于Syensqo首席技术与创新官兼北美区首席官迈克·菲内利而言，这种融合正在改变先进材料所能实现的可能性。“从材料的角度来看，人工智能如今确实正在将半导体和数据中心推向其物理极限，”他说。

随着高温、纯度、电气性能、耐化学性、耐等离子体和长期稳定性等要求不断叠加，材料正朝着菲内利所称的“金字塔顶端”迈进。他认为，先进材料已不仅仅是支撑人工智能创新，“实际上正日益定义着什么将成为可能。”

这一挑战正在为人工智能浪潮提供动力的整个基础设施中上演。Syensqo正在为高压数据中心架构开发材料，为半导体制造开发先进密封材料，以及包括直接浸没冷却流体在内的热管理解决方案。其中一些创新还能跨行业应用。例如，为电动汽车开发的材料可以帮助满足数据中心中正在出现的更高电压和更高能量密度需求。

性能的定义也在发生变化。越来越多的客户期望材料在满足技术需求的同时减少环境影响。“我们的目标是消除性能与可持续性之间的取舍，”菲内利说。这意味着在研究过程的起点就考虑可持续性，而不是在材料开发完成后将其视为附加要求。

人工智能也在改变这些材料的发现方式。Syensqo正在使用人工智能代理对数百万种可能的分子组合进行数字化合成，预测它们的性能和可持续性特征，并将其缩小到一小部分进行实验室测试。菲内利表示，其结果是可以“更广、更深、更快”地推进研究，同时让科学家有更多时间解决复杂的工程问题。

展望未来，菲内利看到了一种强化循环的可能性：人工智能帮助开发改善人工智能基础设施的材料，而这些基础设施反过来又使更好的人工智能加速材料发现。这种反馈回路可能创造一个创新循环，拓展未来技术所能实现的边界。

“你最终会进入这个加速的材料创新循环，”菲内利说。“这让我非常兴奋，它给了我们机会继续推动那些将塑造未来的技术。”

本期Business Lab节目与Syensqo合作制作。

完整文字记录：

梅根·塔特姆：来自《麻省理工科技评论》，我是梅根·塔特姆，这里是Business Lab，一档帮助商业领袖理解从实验室走向市场的新技术的节目。

本期节目与Syensqo合作制作。

如果现在让人们列举人工智能进步的关键推动因素，我们中的许多人可能会列出算法、数据中心，甚至算力，但对性能同样至关重要的是支撑每一层创新的先进材料。随着人工智能的持续演进，它正将半导体和数据中心等推向新的物理极限，给先进材料行业带来跟上步伐的新压力。但这种关系是双向的。当该行业迎接这一挑战时，人工智能也正崛起为加速材料发现和开发的强大工具，显著缩短新解决方案的开发周期。

给你们两个词：材料创新。

今天的嘉宾是迈克·菲内利，Syensqo首席技术与创新官兼北美区首席官。

欢迎你，迈克。

迈克·菲内利：谢谢你，梅根。很高兴来到这里。

梅根：非常感谢你加入我们。迈克，我能否先请你介绍一下Syensqo以及它在开发先进材料方面所扮演的角色？

迈克：好的，当然可以。Syensqo是特种材料领域的全球领导者。我们的工作是帮助客户解决最棘手的技术挑战。我们服务于许多不同的市场，但我喜欢简单地说：如果它能飞，上面就有我们的产品。如果它能跑，里面就有我们的产品。在医疗保健领域，我们的产品每天都在拯救生命。如果你喜欢你的移动设备，如果你喜欢人工智能，正是我们的产品在支撑着生产这一切所需的先进半导体芯片。我们的角色是通过先进化学来实现创新。我们开发的材料具有更高的性能、更强的可靠性，并且越来越可持续。我要说的是，这是我们业务的核心。实际上，它就在我们的名字里——Syensqo。用数字来说明的话，我们年收入的20%来自过去五年中推出的新产品和新应用，这确实证明了我们强大的创新引擎。

梅根：是的，完全同意。正如你刚才描述的，你们涉足各种不同的行业，重点可能在电子和半导体领域。你能再多谈谈这方面的工作以及这些行业的发展方向吗？

迈克：当然。你看，电子和半导体作为Syensqo的战略市场已经有几十年了。我不想暴露年龄，但33年前我刚进入公司时，半导体就是我最早接触的行业之一。我们支持了一波又一波的创新——从实现更小、更强大的移动设备，帮助行业实现越来越小的外形尺寸和芯片。我们帮助推进了超连接，支持日益复杂的半导体制造。今天我们正在帮助推进人工智能时代。

我们拥有业内最广泛的高性能聚合物和先进材料组合之一。我们支持整个电子价值链中的应用，从半导体制造、电子元件到智能设备和电信，甚至超连接。我们的材料正在帮助客户解决围绕小型化、热管理、电气性能、耐化学性、越来越高纯度以及长期可靠性和可持续性方面日益严峻的挑战。今天我们与世界各地的领先半导体制造商和电子公司合作。

梅根：太棒了。正如你刚才提到的，在过去30年里，我们看到了这些行业的巨大演变。

迈克：哦天哪，是的。

梅根：而现在人工智能正在对半导体和数据中心提出这些新要求。这对制造它们所用的材料意味着什么？人工智能创新将在多大程度上受到材料科学找到解决方案的制约或推动？

迈克：是的，你说得完全对。但从材料的角度来看，人工智能如今确实正在将半导体和数据中心推向其物理极限，材料正成为持续进步的关键推动因素。

我试着这样描述它，想象一个金字塔，我称之为性能金字塔。金字塔底部是大宗商品材料，金字塔顶部是高性能特种材料。在Syensqo，我们所做的就是在金字塔顶端运营，我们不断试图通过推出越来越新的、性能越来越高的材料来抬高金字塔的顶端。

你可能会说，好吧，但为什么数据中心或半导体制造工厂需要特种材料而不是大宗商品领域的东西呢？我称之为“而且、而且、而且”原则。如果你只需要一种聚合物或材料能在室温下放在那里十年不变，那么有很多大宗商品材料可以做到，你没有问题。但一旦你开始增加要求，我称之为“而且、而且、而且”——如果你需要一种聚合物能耐高温，而且必须有高纯度，而且有电气性能，而且耐化学性，而且耐等离子体，而且必须具备长期稳定性，所有这些“而且”加在一起，你就开始向金字塔顶端移动了。

现在人工智能在半导体方面所做的，由于它推进的速度，它对半导体芯片和数据中心提出的要求数量在增加，“而且”的数量在增加，这正在推动材料的极限。这就是我们的用武之地。我真的相信先进材料不再只是支撑人工智能创新，我们实际上正日益定义着什么将成为可能。

梅根：对。这太有意思了。在迎接这一挑战、聚焦金字塔顶端和你所说的“而且、而且、而且”原则方面，你能否给我们举一两个你们已经创造或目前正在研究的金字塔顶端解决方案的例子？

迈克：就像我说的，我们的重点是实现更高性能，同时不牺牲可靠性或安全性。我们开发先进聚合物、弹性体、特种流体——流体指的是润滑剂和传热流体——它们被用于整个半导体制造过程，也越来越多地用于人工智能数据中心基础设施。我们在下一代人工智能数据中心特种材料方面的一个例子是围绕高压架构所做的工作。数据中心正在向高压架构转型，因为这可以在提高能源效率的同时实现更强的算力。我们知道这对该行业来说是一个大问题，这些高压架构将帮助他们减少能量损耗、提高能源效率，最终帮助降低数据中心的环境足迹。我们正在开发能帮助他们实现这一目标的新材料。

另一个例子是我们用于半导体工厂和晶圆设备内部的高性能密封材料。如果你能想象一下，很多人都见过半导体在加工过程中的样子。它是一个很大的硅圆盘，之后被切割成进入计算机的微小芯片。但那个晶圆被放入一个巨大的腔室中，那里有非常极端的环境——侵蚀性等离子体、反应性化学品——他们需要越来越高性能的材料。腔室周围所有用于将这些气体保持在内部环境中的密封件都必须能够承受那种环境。这就是我们正在开发的，我们正在推动极限。他们要求更高的温度、更具侵蚀性的环境，同时要求更低的释气和更高的纯度。这就是我们为这个行业开发的，让下一代芯片能够被开发和生产的工业级材料。

梅根：这太有意思了，人们通常不会去关注这种东西里面的密封件。正如你所描述的，在性能方面它绝对是至关重要的。在开发这些解决方案的过程中，我了解到你们也跨不同市场进行研究，看看什么可能适用于不止一个领域，其中包括汽车行业和数据中心之间的重叠，我了解到。你能再多谈谈这方面吗？

迈克：正如我刚才提到的，数据中心正在转向更高电压的架构。这是下一代数据中心，可以更节能，但能量密度更高。功率密度增加，温度也随之升高。我们将要面对的许多材料挑战，我们已经为汽车行业的电动汽车开发过了。我举一个应用例子。想想一辆电动汽车。电动汽车的动力源不再是电机，而是电池。所有能量都在那里。当你把一百千瓦的能量通过电线和所谓的母排传输到电动机时，你要让车很快加速到60英里每小时，你就在传输巨大的能量，温度会急剧升高。

所有的电气连接都在这些母排中，有一种聚合物是绝缘聚合物，中间是铜用于所有连接。它必须承受那种温度升高，而温度升高可能非常迅速。我们在那里开发了新材料，这些材料将可以转化到这些数据中心，那里将有更高的电压和更高的能量密度。

我们在汽车领域一直在做的另一件事是，我们在汽车和半导体方面都有大量关于流体循环以及如何使用介电材料进行直接浸没冷却的知识。这对数据中心和服务器农场非常有价值。用空气冷却半导体效率非常低，而且能耗很大。如果你能把它们浸没在液体中，就是直接浸没冷却，那是极其高效的，所以这是我们正在研究的另一件事。

我们在汽车领域开发的另一项将转化过来的技术是电池储能系统。在电池内部，我们开发了一种粘合剂。它是市场上性能最高的粘合剂，用于锂离子电池的阴极，它能让所有成分各司其职、协同工作，使电池能够持续使用10年并保持性能。现在这正在转移到数据中心，因为它们越来越多地转向可再生能源，需要这些储能系统来平滑峰值负载并提供有韧性的备用电源。这就是我们正在做的事情之一。通过跨市场转移我们的知识，我们可以加速新的电力和新的热管理解决方案，同时支持下一代人工智能基础设施所需的可靠性。

梅根：太棒了。有那么多可转化的应用，这些本来不一定会想到。当然，你们需要应对的不仅仅是技术进步。如今的企业还要求材料以更负责任的方式开发和制造。那么可持续性是如何影响你们的创新过程的？

迈克：是的，你说得完全对。我要说性能仍然是入场券。我们的客户想要性能。现在变化的是，性能的定义更广泛了，它包括可持续性目标和要求。我们的客户期望材料在提供出色技术性能的同时，也能以更负责任的方式开发和制造。

在Syensqo，我们相信作为一家负责任的公司运营意味着我们为客户提供真正的可持续商业解决方案。这就是为什么我们开发了所谓的可持续组合管理工具，简称SPM。它是一个矩阵，定义了什么是可持续解决方案。对我们来说，它是在特定应用中改善我们产品的社会和环境绩效，同时在其生产中展示更低的环境影响，为客户创造价值的产品。简而言之，我们希望开发产品，而这就是起点。我们的每一个研究项目在开始之前都会评估它是否将成为一个可持续产品。

现在我们组合中88%是可持续产品。我们正在开发对环境更好的材料，我们生产时环境足迹更低，同时它们也为客户的改善做出贡献，使他们能够以更低的碳足迹运营，或以更安全的方式运营，或减少水消耗。里面有很多不同的类别。

另一个例子是我们对下一代传热流体的长期开发。半导体制造和数据中心变得更加强大。我之前提到它们产生的热量，特别是当它们转向更高电压架构时。管理这些热量变得越来越重要。再说一次，我谈到了直接浸没冷却。我们正在开发这些解决方案，因为目前市面上有可以用的流体，但它们的全球变暖潜值很高。这对环境不利。我们正在开发下一代传热流体，与当今的流体相比将减少潜在的环境影响。最终，我们的目标是消除性能与可持续性之间的取舍。你注意到这又是一个“而且”——我们可以既有性能又可持续。

梅根：这太重要了，不是吗？从性能的角度来考虑可持续性。正如你所说，当我们考虑将这些解决方案商业化规模推广时，这是非常重要的一部分。正如我在介绍中提到的，人工智能不仅是一个挑战，也是先进材料领域的一个机遇。我很想了解你们在Syensqo如何利用人工智能工具来辅助和加速解决方案的开发。

迈克：当然。我们大约两年前开始了这段旅程，我们在研发中使用人工智能，并与微软及其Microsoft Discovery工具合作，它帮助我们快速识别和评估有前景的候选分子。

在传统的研究方法中，历史上你会设计实验，然后查看你可以制造所有这些不同分子的材料和化学品的所有潜在组合。你可以开发来解决问题的潜在分子组合可能有数百万种，但在实验室中开发一百万或数千万个分子并实际物理操作是不可能的。你必须根据你的专业知识和知识、根据文献检索、根据现有技术水平和查看专利等，选择一个小范围。你选一个小区域，然后走流程，开发材料，测试它们，学到一些东西，回到绘图板，重新开始。最终你找到可行的东西，但这并不意味着你找到了现有最好的可能组合。

但我们用人工智能所做的是，我们与微软一起开发了人工智能代理，它们实际上在数字化合成数百万乃至数百万种潜在分子的全部组合。我们有另一个人工智能代理使用基于物理的模拟来查看所有这些分子并预测它们的性能，不仅是物理化学性能，还有毒性、可持续性等。然后我们有另一个代理获取所有信息并对它们进行排名。最终，我们探索了所有潜在的分子，我们大致了解性能应该是什么样，我们最终得到一个可能一百个的优先列表——而不是数百万个——一百个我们实际在实验室合成的分子。

最终，你更快地得到解决方案，快得多。你探索了整个空间。我基本上说这让我们能够更广、更深、更快。重要的是，它不是取代我们的科学家，不是取代我们的科学专业知识。在某种程度上，它给了他们超能力。它让他们花更少的时间搜索，更多的时间解决行业最棘手的工程挑战。

梅根：太了不起了。从你的解释来看，这听起来确实是一个真正具有变革性的工具。

迈克：完全是，完全是。

梅根：我的意思是，迈克，最后如果能展望一下未来就太好了，因为人工智能和先进材料领域都有如此多的活动。我想知道接下来你最期待的是什么？

迈克：我谈了很多关于人工智能以及我们如何使用人工智能来开发新材料。对我来说，真正令人兴奋的——我开始看到它实际发生了，我只是好奇这会有多快——是我们正在使用人工智能开发新材料，这些材料将使人工智能变得更好，然后那个人工智能将使用新的人工智能来开发新材料，让人工智能变得更好。我看到了这个循环：为人工智能开发，让人工智能改进，然后我们使用那个人工智能来改进我们自己。你最终进入这个加速的材料创新循环。这让我非常兴奋，它给了我们机会继续推动那些将塑造未来的技术。这就是我们在Syensqo所做的。

梅根：太棒了。是的，真正的创新良性循环，听起来就是这样。太了不起了。非常感谢你，迈克。

迈克：谢谢。

梅根：非常感谢。那是迈克·菲内利，Syensqo首席技术与创新官兼北美区首席官，我在英格兰布莱顿对他进行了采访。

这就是本期Business Lab的内容。我是你的主持人梅根·塔特姆。我是《麻省理工科技评论》定制出版部门Insights的特约编辑和主持人。我们于1899年在麻省理工学院创立，你可以在印刷品、网络和每年世界各地的活动中找到我们。如需了解更多关于我们和节目的信息，请访问我们的网站technologyreview.com。

本节目可在你获取播客的任何平台收听，如果你喜欢，希望你能花点时间给我们评分和评论。Business Lab是《麻省理工科技评论》的制作，本期节目由Giro Studios制作。非常感谢收听。再见。

本内容由《麻省理工科技评论》定制内容部门Insights制作，非其编辑团队。它由人工研究和撰写，可能使用的任何人工智能工具仅限于在人工监督下的制作流程。

深度探索

人工智能

一个根本性缺陷使大语言模型极易受到攻击

它让人很容易诱骗它们做出不该做的事情，比如告诉你如何破坏飞机的导航系统。

人工智能在招聘时比人类更容易形成偏见

人工智能不仅从训练中学习刻板印象，它还能制造新的刻板印象。

人工智能的递归自我改进可能不会那么快到来

人工智能代理似乎还不够有创造力，无法开展真正创新的开放式人工智能研究。

以下是人工智能代理为达成目标而撒谎和作弊的原因

这种行为被称为奖励黑客。这是你需要了解的。

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英文来源：

Sponsored
Building the materials foundation for AI
As AI pushes semiconductors and data centers toward new physical limits, advanced materials are becoming critical to performance, efficiency, and sustainability, says Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo.
In partnership withSyensqo
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. Semiconductors and data centers are approaching physical limits around performance, thermal management, electrical efficiency, and reliability, creating new demands for materials that can do more at once. At the same time, AI is giving materials scientists new ways to search the enormous universe of possible molecules and accelerate the development of solutions.
For Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, that convergence is transforming what advanced materials can enable. “AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits,” he says.
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.” Beyond supporting AI innovation, he contends that advanced materials are “actually increasingly defining what's going to be possible.”
That challenge is playing out across the infrastructure powering the AI surge. 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 also cross industry boundaries. Materials developed for electric vehicles, for example, can help address the higher voltage and energy-density demands that are emerging in data centers.
The definition of performance is also changing. More customers are expecting materials to meet technical requirements while reducing environmental impact. “Our goal is to remove the trade-off between performance and sustainability,” Finelli says. That 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 those 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. The result, Finelli says, is the ability to go “broader, deeper, and faster” while giving scientists more time to solve complex engineering problems.
Looking to the future, Finelli sees the possibility of a reinforcing cycle: AI helps develop materials that improve AI infrastructure, which in turn enables better AI to accelerate materials discovery. That feedback loop could create a cycle of innovation and expand what future technologies can achieve.
“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.”
This episode of Business Lab is produced in partnership with Syensqo.
Full Transcript:
Megan Tatum: From MIT Technology Review, I'm Megan Tatum, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
This episode is produced in partnership with Syensqo.
Now asked to name the key enablers to AI advancement, many of us might list algorithms, data centers, or even computing power, but just as critical to the performance are the advanced materials that underpin each layer of that innovation. As AI continues to evolve, it's pushing the likes of semiconductors and data centers to new physical limits, putting new pressure on the advanced material sector to keep pace. But the relationship goes both ways. As the sector rises to this challenge, AI is also emerging as a powerful tool for accelerating materials discovery and development, significantly shortening development timelines for new solutions.
Two words for you: materials innovation.
My guest today is Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo.
Welcome, Mike.
Mike Finelli: Thank you, Megan. Nice to be here.
Megan: Thank you so much for joining us. Mike, can I start by asking you to tell us a little bit more about Syensqo and the role it plays in developing advanced materials?
Mike: Yeah, absolutely. Syensqo is a global leader in specialty materials. Our job is to help customers solve their toughest technology challenges. We serve a lot of different markets, but the way I like to say it simply is if it flies, we're on it. If it drives, we're in it. In healthcare, our products literally are saving lives every day. And if you like your mobile devices, if you like AI, it's our products that are actually enabling the advanced semiconductor chips that are required to produce all of this. Our role is to enable innovation through advanced chemistry. We develop materials that deliver higher performances, greater reliability, and increasingly more sustainable solutions. The way I would say this, it's at the heart of our business. Actually, it's in our name, Syensqo. And to put some numbers around it, 20% of our annual revenues come from new products and applications that we've launched in the last five years, which is really evidence of a really strong innovation engine.
Megan: Yeah, absolutely. And as you sort of described there, you're in all sorts of different industries with an emphasis perhaps on electronics and semiconductors. Can you talk a bit more about that work and where those industries are headed perhaps?
Mike: Sure. So look, electronics and semiconductors have been strategic markets for Syensqo for literally decades. I don't want to date myself, but 33 years ago when I started in the company, semiconductors were one of the first industries that I worked in. And we've supported successive waves of innovation from enabling smaller, more powerful mobile devices, helping the industry get to the smaller and smaller profiles and the chips. We've helped to advance hyperconnectivity, supporting increasingly sophisticated semiconductor manufacturing. And today we're helping to advance the AI era.
We have one of the industry's broadest portfolios of high performance polymers and advanced materials. We support applications across the entire electronics value chain from semiconductor fabrication, electronic components, to smart devices and telecommunications, even hyperconnectivity. And our materials are helping customers solve increasingly demanding challenges around miniaturization, thermal management, electrical performance, chemical resistance, higher and higher purities, and long-term reliability and sustainability. And today we work with leading semiconductor manufacturers and electronics companies all around the world.
Megan: Fantastic. And as you alluded to there in the last 30 years, we've seen huge evolutions in those sectors.
Mike: Oh my God, yes.
Megan: And now AI is putting these new demands on semiconductors and data centers. What does that mean for the materials they're built from and to what extent will AI innovation be constrained or enabled by materials science finding a solution?
Mike: Yeah, so I mean, you're absolutely right. But AI is now, from a material standpoint, really pushing semiconductors and the data centers to their physical limits, and materials are becoming a key enabler of that continued progress.
The way I try to describe it, think of a pyramid, I call it the performance pyramid. You have commodity materials at the bottom of the pyramid and you have high performing specialty materials at the top of the pyramid. At Syensqo, all we do is we operate at the top of the pyramid and we're continually trying to raise the top of that pyramid by bringing newer and newer and more higher performing materials out.
Now you might say, okay, but why doesn't a data center or a semiconductor manufacturing fab need a specialty versus something in the commodity space? Well, I call it the and, and, and principle. If you just need a polymer or a material that can sit at the table at room temperature and stay there for 10 years and not change, well, there's a lot of commodity materials that will do that and you don't have a problem. The minute you start adding requirements, and I call it the and, and, and so if you need a polymer that can handle high temperature and have to have high purity and electrical performance and chemical resistance and plasma resistance and it's got to have long-term stability, all of these ands, you start moving to the top of the pyramid.
Now what AI is doing with semiconductors, because of the speed at which it's advancing, it's requiring semiconductor chips and data centers, the number of requirements are increasing the number of ands which is pushing the limits of the materials. That's where we come in. And I really believe that advanced materials, they're no longer just supporting AI innovation, we're actually increasingly defining what's going to be possible.
Megan: Right. That's fascinating. And in terms of rising to that challenge of focusing on that top of the pyramid and that and, and, and principle you're talking about, could you talk us through perhaps an example or two of those top of the pyramid solutions you've created or that you're working on at the moment?
Mike: Like I said, our focus is enabling higher performance, but it's also without compromising on reliability or safety. We develop advanced polymers, elastomers, specialty fluids, fluids meaning lubricants and heat transfer fluids, and they're used throughout the semiconductor manufacturing process and also increasingly in AI data center infrastructure. One example of our work on specialty materials for next generation AI data centers is the work we're doing around high voltage architectures. Data centers are moving towards high voltage architectures because they can enable greater computing power while also improving energy efficiency. We know that's a big issue for that segment of the industry, and these high voltage architectures will help them reduce and improve energy efficiency because it reduces energy losses and they can ultimately help lower the environmental footprint of the data centers. And we're developing new materials that can help them get there.
Another example is our high performing sealing materials found inside semiconductor fabs and wafer tools. If you can picture, many people have seen what a semiconductor looks like during processing. It's a big, big silicon disc that's then later diced into the tiny little chips that go into the computer. But that wafer is put inside a giant chamber where it has a very extreme environment, aggressive plasmas, reactive chemicals, and they need higher and higher performing materials. And all of the seals that are around that chamber to keep those gases in the environment inside have to be able to withstand that environment. And that's what we're developing and we're pushing the limits. They're asking for higher temperatures, more aggressive environment with lower out gassing and purity. And that's what we're developing for this industry to allow that next chip to be developed and produced industrial.
Megan: It's so fascinating that people wouldn't give much though necessarily to the seal in something like that. As you're outlining, it's just absolutely critical in terms of performance. And in developing those solutions, I understand you also looked across different markets to see what may be applicable perhaps in more than one space, and that includes an overlap between the automotive sector and data centers, I understand. Can you tell us a little bit more about that?
Mike: As I mentioned just previously, the data centers are shifting to higher voltage architectures. This is the next generation data center, which can be more energy efficient, but it's got a higher energy density. The power density increases, which increases temperatures. And many of the material challenges that we will be facing there, we've already developed for the automotive industry in electric vehicles. I'll give you an example of an application. I mean, think about an electric vehicle. The powerhouse in electric vehicle is no longer the motor, it's the battery. That's where all the energy sits. And when you're putting a hundred kilowatts of energy, driving that to the electric motor through wires and through what they call bus bars, you got to get that car up to 60 miles an hour pretty quick. You're driving massive amounts of energy that's increasing temperatures dramatically.
And all the electrical connections are in these bus bars that there's a polymer that's an insulating polymer with copper in between for all the connections. That's got to withstand that temperature increase, which could come pretty rapidly. We've developed new materials there and those materials will be translatable over to these data centers where they're going to have the higher voltages with a higher energy density.
Another thing we've been doing in automotive, we have a lot of knowledge in both automotive and semiconductor around fluid circulation and how to use dielectric materials to do direct immersion cooling. That's something that will be very valuable for data centers and server farms. Using air to cool semiconductors is really inefficient and energy intensive. If you could submerse them in a liquid, you have direct immersion cooling, that's extremely efficient, so that's another thing we're working on.
Another thing we developed in automotive that will be translated over is battery energy storage systems. Inside the battery, we've developed a binder. It's the highest performing binder on the market, which is using the cathode of a lithium ion battery, and it keeps all the ingredients doing its job working together so that battery can actually last for 10 years and perform. Now that's moving over to the data centers because they're moving more towards renewables and they need to have these energy storage systems to smooth the peak loads and provide resilient backup power. That's one of the things that we're doing. By transferring our knowledge across the markets, we can accelerate new power and new thermal management solutions while supporting reliability required by next generation AI infrastructure.
Megan: Fantastic. So many transferable applications there that necessarily wouldn't have sprung to mind. And it isn't only technical advancements that you need to contend with, of course. Companies today are also demanding the materials are developed and manufactured more responsibly too. So how is sustainability shaping your innovation process?
Mike: Yeah, you're absolutely right. I will say performance is still the entry ticket. Our customers want performance. Now what's changing is that definition of performance is now broader and it is including sustainability targets and requirements. Our customers expect materials that deliver outstanding technical performance while also being developed and manufactured more responsibly.
At Syensqo, we believe that operating as a responsible company means we're providing true sustainable business solutions to our customers. And this is why we developed what we call the Sustainable Portfolio Management tool, SPM. It's a matrix, and it defines what a sustainable solution is. For us, it's a product that in a given application improves our product's social and environmental performance while also demonstrating a lower environmental impact in its production, creating values for our customers. In short, we want to develop products, and this is where it starts. Every one of our research projects before we even start them is assessed on whether it's going to be a sustainable product or not.
And 88% of our portfolio now is a sustainable product. We're developing materials that are better for the environment, lower environmental footprint when we produce it, but also they contribute to improvements for our customers as well so they could operate with a lower carbon footprint or they can operate in a safer way or less water consumption. There's a lot of different lists in there.
Another example is our longer-term development of next generation heat transfer fluids. Semiconductor manufacturing and data centers have become more powerful. I mentioned before the heat that they're generating, especially when they move to the higher voltage architectures. Managing that heat is increasingly important. And again, I talked about direct immersion cooling. We're developing those solutions because today there are fluids out there that will work, but they got high global warming. That's not good for the environment. We're developing the next generation heat transferred fluids that will reduce the potential environmental impact compared to the fluids today. In the end, our goal is to remove the trade-off between performance and sustainability. You notice that's another and, we can be performing and sustainable.
Megan: That's so important, isn't it though, to think about sustainability in terms of performance? As you say, when we're thinking about commercially scaling up these solutions, it's such an important part of it. And as I talked about in the introduction, AI isn't only a challenge, but it's also an opportunity within the advanced material space. I'd love to explore how you're using AI tools at Syensqo to inform and accelerate the development of solutions as well.
Mike: Absolutely. We embarked on this journey about two years ago, where we're using AI in our research and development, and we've partnered with Microsoft and their Microsoft discovery tool, and it's helping us to rapidly identify and evaluate promising molecular candidates.
Now, in the normal research approach, historically, you would design your experiment and you'd look at all the potential combinations of materials and chemicals that you could make all these different molecules. And the combinations of potential and molecules that you could develop to solve a problem could be in the millions, but it's impossible to develop a million molecules or tens of millions of molecules in your laboratory and actually physically do that. But you have to select a small area based on your expertise and knowledge, based on the literature searches, based on the state of the art that's out there and looking at patents, et cetera. And you pick a small area and you go through the process, you develop the materials, you test them, you learn something, you go back to the drawing board, you start again. Eventually you find something that works, but it doesn't mean you found the best possible combination that's out there.
But what we're doing with AI is we have developed AI agents with Microsoft that are literally digitally synthesizing the entire millions and millions of combinations of potential molecules. And we have another AI agents that are using physics-based simulation to look at all those molecules and predict the performance of them, and not just performance on physical chemical properties, but also on toxicity, on sustainability, et cetera. Then we have another agent that takes all that information and ranks them all. In the end, we have explored all of the potential molecules out there. We understand roughly what the performance should be, and we end up with a priority list of maybe a hundred, instead of millions and millions, a hundred that we actually synthesize in the lab.
And at the end, you end up getting the solution faster, much, much faster. You've explored the entire space. I basically say it allows us to go broader, deeper, and faster. And the important thing is it's not replacing our scientists, it's not replacing our scientific expertise. In a way, it's giving them superpowers. It's allowing them to spend less time searching and more time solving the industry's toughest engineering challenges.
Megan: Amazing. It sounds like it's genuinely a really transformative tool by what you're explaining.
Mike: Completely, completely.
Megan: I mean, just to finish, Mike, it'd be great to take a look ahead if we could, because there's so much activity in both AI and the advanced material space. I wonder what is coming down the pipeline that you are most excited about next?
Mike: I've talked a lot about AI and how we're using AI to develop new materials. I think to me, what's really exciting, and I'm starting to see it actually happen, I'm just curious how fast this is going to go, is that we're using AI to develop new materials that will enable AI to get better, and then that AI will use the new AI to develop new materials to get AI to go better. I see this loop of developing for AI, for AI to improve, and then we use that AI to improve ourselves. You end up in this accelerated materials, innovative cycle of materials innovation. That really excites me, and it gives us the opportunity to continue enabling technologies that will shape the future. That's what we do at Syensqo.
Megan: Fantastic. Yeah, real sort of virtuous circle of innovation, it sounds like that. Amazing. Thank you so much, Mike.
Mike: Thank you.
Megan: Thank you so much. That was Mike Finelli, chief technology and innovation officer and chief North America officer at Syensqo, whom I spoke with from Brighton in England.
That's it for this episode of Business Lab. I'm your host, Megan Tatum. I'm a contributing editor and host for Insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can find us in print on the web and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
This show is available wherever you get your podcasts, and if you enjoyed it, we hope you'll take a moment to rate and review us. Business Lab is a production of MIT Technology Review, and this episode was produced by Giro Studios. Thanks so much for listening. Goodbye.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
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