Theoretical Prediction of Superhard Materials with the XtalOpt Evolutionary Algorithm

The XtalOpt evolutionary algorithm for crystal structure prediction has been extended to enable the prediction of superhard stable materials. The hardness is calculated via a linear relationship with the shear modulus (originally discovered by Teter) as reported by Chen. The shear modulus is obtained via AFLOW-ML (Automatic FLOW for Materials Discovery - Machine Learning). A new fitness function has been implemented wherein the user can denote the percent contribution that hardness and enthalpy have on the fitness function. We have used XtalOpt to search for hard and stable carbon allotropes and found 44 hitherto unpredicted phases whose Vickers Harnesses were calculated to be greater than 45 GPa. The structural motifs in these phases were analyzed. We also discuss the thermodynamic and kinetic stability of the predicted structures, and potential ways in which they can be synthesized under pressure.