Ultrahard
materials are an essential component in a wide range
of industrial applications. In this work, we introduce novel machine
learning (ML) features for the prediction of the elastic moduli of
materials, from which the Vickers hardness can be calculated. By applying
the trained ML models on a space of ∼110,000 materials, these
features successfully predict the elastic moduli for a range of materials.
This enables the identification of materials with high Vickers hardness,
as validated by comparing the predictions against the density functional
theory calculations of the moduli. We further explored
the predicted moduli by examining several classes of materials with
interesting mechanical properties, including binary and ternary alloys,
aluminum and magnesium alloys, metal borides, carbides and nitrides,
and metal hydrides. Based on our ML models, we identify a number of
ultrahard compounds in the B–C and B–C–N chemical
spaces and ultrahard ultralight-weight magnesium alloys Mg3Zn and Mg3Cd. We also observe the inverse of the hydrogen
embrittlement effect in a number of metal carbides, where the introduction
of hydrogen into metal carbides increases their hardness, and find
that substitutional doping of Al in transition-metal borides can yield
lighter materials without compromising the thermodynamic stability
or the hardness of the material.