Superhard materials are highly prized, ironically enough, for their flexibility. Not in terms of bending, but rather in terms of what they can be used to build. Creating scratch-resistant coatings, for example, could have any number of uses. So finding more of these materials is a priority for scientists, which is why a team from the University of Buffalo used artificial intelligence to identify 43 previously unknown forms of carbon that are thought to be stable and superhard. The 43 carbon structures are still theoretical, meaning that scientists have predicted them, but haven't actually brought them forward into creation yet.

The computational prediction of superhard materials would enable the in silico design of compounds that could be used in a wide variety of technological applications. Herein, good agreement was found between experimental Vickers hardnesses, Hv, of a wide range of materials and those calculated by three macroscopic hardness models that employ the shear and/or bulk moduli obtained from: (i) first principles via AFLOW-AEL (AFLOW Automatic Elastic Library), and (ii) a machine learning (ML) model trained on materials within the AFLOW repository. Because \(H_{\mathrm{v}} {{\mathrm{ML}}}\) values can be quickly estimated, they can be used in conjunction with an evolutionary search to predict stable, superhard materials. This methodology is implemented in the XtalOpt evolutionary algorithm. Each crystal is minimized to the nearest local minimum, and its Vickers hardness is computed via a linear relationship with the shear modulus discovered by Teter.