An illustration depicts three of 43 newly predicted superhard carbon structures. The cages colored in blue are structurally related to diamond, and the cages colored in yellow and green are structurally related to lonsdaleite. Credit: Bob Wilder / University at Buffalo
Researchers have used computational techniques to identify 43 previously unknown forms of carbon that are thought to be stable and superhard -- including several predicted to be slightly harder than or nearly as hard as diamonds. Each new carbon variety consists of carbon atoms arranged in a distinct pattern in a crystal lattice.
The study -- published in the journal npj Computational Materials -- combines computational predictions of crystal structures with machine learning to hunt for novel materials. The work is theoretical research, meaning that scientists have predicted the new carbon structures but have not created them yet.
Eva Zurek, a University at Buffalo professor of chemistry, conceived of the study and co-led the project with Stefano Curtarolo, PhD, professor of mechanical engineering and materials science at Duke University.
Superhard materials can slice, drill and polish other objects. They also hold potential for creating scratch-resistant coatings that could help keep expensive equipment safe from damage.
"Diamonds are right now the hardest material that is commercially available, but they are very expensive," says Zurek. "I have colleagues who do high-pressure experiments in the lab, squeezing materials between diamonds, and they complain about how expensive it is when the diamonds break.
She added, "We would like to find something harder than a diamond. If you could find other materials that are hard, potentially you could make them cheaper. They might also have useful properties that diamonds don't have. Maybe they will interact differently with heat or electricity, for example."
The first and second authors of the new study are UB PhD graduate Patrick Avery and UB PhD student Xiaoyu Wang, both in Zurek's lab. In addition to these researchers, Zurek, Curtarolo and Toher, the co-authors of the paper include Corey Oses and Eric Gossett of Duke University and Davide Proserpio of the Universitá degi Studi di Milano.
The research was funded by the U.S. Office of Naval Research, with additional support from the Universitá degi Studi di Milano, and computational support from UB's Center for Computational Research.