New material combines light weight with high strength

In a new paper published in Advanced Materials, a team led by Professor Tobin Filleter at the University of Toronto describes how they made nanomaterials with properties that offer exceptional strength, light weight and customisation. The approach could benefit a wide range of industries including automotive and aerospace.  

“Nano-architected materials combine high performance shapes, like making a bridge out of triangles, at nanoscale sizes, which takes advantage of the ‘smaller is stronger’ effect, to achieve some of the highest strength-to-weight and stiffness-to-weight ratios, of any material,” said Peter Serles, the first author of the new paper.  

“However, the standard lattice shapes and geometries used tend to have sharp intersections and corners, which leads to the problem of stress concentrations. This results in early local failure and breakage of the materials, limiting their overall potential.  

“As I thought about this challenge, I realised that it is a perfect problem for machine learning to tackle.”  

Nano-architected materials are made of repeating units measuring a few hundred nanometres in size. These building blocks, which are composed of carbon, are arranged in complex 3D nanolattices.   

To design their improved materials, Serles and Filleter worked with Professor Seunghwa Ryu and PhD student Jinwook Yeo at the Korea Advanced Institute of Science & Technology (KAIST) in Daejeon, South Korea.  

The KAIST team employed the multi-objective Bayesian optimisation machine learning algorithm, which learned from simulated geometries to predict the best possible geometries for enhancing stress distribution and improving the strength-to-weight ratio of nano-architected designs.  

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Serles then used a two-photon polymerisation 3D printer housed in the Centre for Research and Application in Fluidic Technologies (CRAFT) to create prototypes for experimental validation. This additive manufacturing technology enables 3D printing at the micro and nano scale, creating optimised carbon nanolattices.  

These optimised nanolattices are said to have more than doubled the strength of existing designs, withstanding a stress of 2.03 megapascals for every cubic metre per kilogram of its density, which is about five times higher than titanium.

“This is the first time machine learning has been applied to optimise nano-architected materials, and we were shocked by the improvements,” Serles said in a statement. “It didn’t just replicate successful geometries from the training data; it learned from what changes to the shapes worked and what didn’t, enabling it to predict entirely new lattice geometries.  

“Machine learning is normally very data intensive, and it’s difficult to generate a lot of data when you’re using high-quality data from finite element analysis. But the multi-objective Bayesian optimisation algorithm only needed 400 data points, whereas other algorithms might need 20,000 or more. So, we were able to work with a much smaller but an extremely high-quality data set.”   

“We hope that these new material designs will eventually lead to ultra-light weight components in aerospace applications, such as planes, helicopters and spacecraft that can reduce fuel demands during flight while maintaining safety and performance,” said Filleter. “This can ultimately help reduce the high carbon footprint of flying.”  

“For example, if you were to replace components made of titanium on a plane with this material, you would be looking at fuel savings of 80 litres per year for every kilogram of material you replace,” said Serles.  

Next steps will focus on further improving the scale up of these material designs to enable cost effective macroscale components.   

“In addition, we will continue to explore new designs that push the material architectures to even lower density while maintaining high strength and stiffness,” said Filleter.