Researchers have created a strong, lightweight carbon nanostructure that could have significant implications for aviation and other industries. This new material, developed using a multi-objective Bayesian optimization (MBO) algorithm, exhibits impressive strength-to-weight ratios, potentially surpassing existing materials.
Nanostructured metamaterials have demonstrated considerable potential in laboratory settings. However, their practical applications have often been limited by stress concentration issues. In this study, researchers leveraged an AI algorithm trained on finite element analysis (FEA) data to identify optimal nanostructure configurations. This AI-driven approach led to the design of a novel carbon nanostructure.
The selected design was then fabricated using 2-photon polymerization (2PP) photolithography. The resulting carbon nanolattices achieve the compressive strength of carbon steels, ranging from 180 to 360 MPa, but with a density comparable to Styrofoam, between 125 and 215 kg m−3. This combination translates to specific strengths that are an order of magnitude greater than existing low-density materials.
While the material is still in the early stages of development, its potential applications are promising. Aviation is one area where lighter and stronger materials are highly sought after, as they can lead to significant fuel or energy savings. The research also shows that AI can go beyond generating images and can, in fact, be a powerful tool for material discovery and optimization.
This discovery adds to recent developments in the field, such as AI-assisted battery material identification, and suggests that there may be more avenues where AI can push the boundaries of scientific research.
As with any laboratory breakthrough, it will require further development before widespread use becomes possible.
