In the pursuit of materials with
exceptional mechanical properties,
a machine-learning model is developed to direct the synthetic efforts
toward compounds with high hardness by predicting the elastic moduli
as a proxy. This approach screens 118 287 compounds compiled
in crystal structure databases for the materials with the highest
bulk and shear moduli determined by support vector machine regression.
Following these models, a ternary rhenium tungsten carbide and a quaternary
molybdenum tungsten borocarbide are selected and synthesized at ambient
pressure. High-pressure diamond anvil cell measurements corroborate
the machine-learning prediction of the bulk modulus with less than
10% error, as well as confirm the ultraincompressible nature of both
compounds. Subsequent Vickers microhardness measurements reveal that
each compound also has an extremely high hardness exceeding the superhard
threshold of 40 GPa at low loads (0.49 N). These results show the
effectiveness of materials development through state-of-the-art machine-learning
techniques by identifying functional inorganic materials