AI in Materials Manufacturing: How generative AI is transforming materials manufacturing by replacing complex equations with faster, smarter, and more scalable design and discovery methods.
In science and engineering, we often operate under a comforting assumption: if we know the governing equations, we can simulate the system. The truth is far less simple. In manufacturing, systems are often so complex that solving the equations becomes practically impossible, even with the latest supercomputers.
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This challenge is most evident in the design and manufacture of advanced materials. Whether we’re talking about batteries, bone implants, composites, or green cement, the performance of many critical materials depends on their microstructure. These microscopic features are far too small to see with the naked eye, yet far larger than atoms. These in-between scales govern strength, conductivity, durability, and more.
Lithium-ion batteries are central to the future economy, enabling the clean energy transition while powering the digital infrastructure, consumer devices, and industrial automation that drive modern commerce. Designing a high-performance cell isn’t just about choosing the right chemistry; it’s about engineering the microstructure of the electrodes. The processes that determine this microstructure include mixing, coating, drying, and calendaring. These steps are wildly complex, involving fluid flow, evaporation, cracking, and collisions. Simulating these steps using traditional physics-based models is a monumental challenge. The best available methods generally still simulate a cartoon version involving perfect spheres neatly bouncing off each other.
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Unsurprisingly, this generates microstructures that look very little like real materials.
At Polaron, a spin-out from Imperial College London that I co-founded in 2023, we are using generative AI to tackle this problem from a new angle. Instead of trying to simulate microstructure formation using physics equations, we use image-based generative models to learn the relationships directly from data. In our latest research, we’ve developed “conditionalized generative AI” models that capture the effects of manufacturing parameters such as drying temperature or slurry composition on the resulting microstructure.
The synthetic microstructures generated by these models are nearly indistinguishable from real microscopy images, giving us greater confidence in the insights they provide.
These models are also remarkably fast, operating orders of magnitude faster than traditional simulations. That speed allows us to run thousands of virtual experiments to explore how changes in manufacturing settings impact important metrics like surface area or the wiggliness of transport pathways. These insights can then feed into well-established battery models.
Ultimately, this means we can design batteries that charge faster, last longer, and are tailored to specific applications.
This approach extends beyond batteries. The same principles apply to other material systems where performance hinges on complex microstructures: green cement, aerospace composites, and advanced alloys. In all of these cases, the manufacturing process is a chaotic, multiphysics problem that defies traditional modelling. Generative AI offers a shortcut not by ignoring the physics but by learning its effective behavior directly from the data.
The idea that “we know the equations, therefore we can simulate” is seductive but incomplete. In many systems, the equations are simply too complex, the parameter space too vast, and the computation too expensive. Generative AI is rewriting the rules, offering a pragmatic and scalable way to capture the essence of complex physical systems.
For the future of materials manufacturing, that’s revolutionary.
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