A coating that can hide objects in plain sight, or an implant that behaves exactly like bone tissue. These extraordinary objects are already made from ‘metamaterials’. Researchers from TU Delft have now developed an AI tool that not only can discover such extraordinary materials but also makes them fabrication-ready and durable. This makes it possible to create devices with unprecedented functionalities. They publish their findings in Advanced Materials, says TU Delft in a press release.
Why you need to know this:
The development of the AI tool by researchers at TU Delft aimed at discovering, designing and producing metamaterials offers several advantages and opportunities in different application areas. Think of surgical instruments or exoskeletons.
Design of metamaterials
The properties of normal materials, such as stiffness and flexibility, are determined by the molecular composition of the material, but the properties of metamaterials are determined by the geometry of the structure from which they are built. Researchers design these structures digitally and then have it 3D-printed. The resulting metamaterials can exhibit unnatural and extreme properties. Researchers have, for instance, designed metamaterials that, despite being solid, behave like a fluid.
“Traditionally, designers use the materials available to them to design a new device or a machine. The problem with that is that the range of available material properties is limited. Some properties that we would like to have, just don’t exist in nature. Our approach is: tell us what you want to have as properties and we engineer an appropriate material with those properties. What you will then get, is not really a material but something in-between a structure and a material, a metamaterial”, says professor Amir Zadpoor of the Department of Biomechanical Engineering.
Such a materials discovery process requires solving a so-called inverse problem: the problem of finding the geometry that yields the desired properties. These inverse problems are notoriously difficult, which is where AI comes in. Researchers at TU Delft have developed deep learning models that solve these inverse problems.
“Even when inverse problems were solved in the past, they were limited by the simplifying assumption that small-scale geometry can be made from an infinite number of building blocks. The problem with that assumption is that metamaterials are usually made by 3D printing and real 3D printers have limited resolution, which limits the number of building blocks that can fit into a device,” says first author Dr Helda Pahlavani.
The AI models developed by the TU Delft researchers break new ground by bypassing these simplifying assumptions. “So we can now simply ask: how many building blocks can you incorporate into the device with your manufacturing technique? The model then finds the geometry that gives the desired properties for the number of building blocks you can actually make.”
Exploiting full potential
A major practical problem under-researched in previous research was the durability of metamaterials. Most existing designs break down once they have been used a few times. “Until now, it has only been about the properties that can be achieved. Our study takes durability into account and selects the most durable designs from a large pool of design candidates. This makes our designs truly practical and not just theoretical adventures,” says Zadpoor.
The possibilities of metamaterials seem endless, but the full potential is far from being realised, says associate professor Mohammad J. Mirzaali. This is because currently, finding the optimal design of a metamaterial is still largely based on intuition, involves trial and error and is therefore labour-intensive. The use of a reverse design process, where the desired properties are the starting point of the design, is still rare within the metamaterial field. “But we think the step we have taken is revolutionary for metamaterials. It can lead to all kinds of new applications.” The researchers see potential applications in orthopaedic implants, surgical instruments, soft robots, adaptive mirrors and exo-skeletons.