Superhard materials are commonly used to slice, drill and polish other objects. They hold potential for creating scratch-resistant coatings that could help keep expensive equipment safe from damage.
A team of researchers has used computational techniques to identify 45 previously known forms of carbon that are thought to be stable and tough. Several of them are predicted to be harder than or nearly as hard as diamonds. Each new variety of carbon contains carbon atoms arranged in a distinct pattern in a crystal lattice, similar to the atom arrangement in the diamond.
The study combines computational predictions of crystal structures with machine learning to look for novel material. The work is a theoretical effort and the creation of these super hard carbon objects is still to be done.
“Diamonds are right now the hardest material that is commercially available, but they are very expensive,” says University at Buffalo chemist Eva 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.
“We would like to find something harder than a diamond. If you could find other hard materials, 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 quest for hard materials
Scientists consider a substance to be extremely hard if it has a hardness value of over 40 gigapascals. The material is measured through an experiment called the Vickers hardness test. All 43 new carbon structures are predicted to meet this limit and three of them are expected to exceed the Vickers hardness of diamonds, only by a slight percentage.
The hardest structures the scientists found contained parts of diamond and lonsdaleite—also called hexagonal diamond—in their crystal lattices. In addition to the 43 novel forms of carbon, the research also predicts that several carbon structures that other teams have described in the past will be super hard.
The technique used in the new paper could be used to identify other super-hard materials that contain elements other than carbon. The researchers used XtalOpt which is an open-source evolutionary algorithm for crystal structure prediction developed in Eva Zurek’s Lab. The team used the algorithm to generate crystal structures for carbon. Aster that the team used a machine learning model to predict the hardness of these carbon species. Researchers say that this accelerated material development. If the model is trained well, it will predict the properties of a material with reasonable accuracy.