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Informatics-Driven Design of Superhard B–C–O Compounds
Materials containing
B, C, and O, due to the advantages of forming
strong covalent bonds, may lead to materials that are superhard, i.e.,
those with a Vicker’s hardness larger than 40 GPa. However,
the exploration of this vast chemical, compositional, and configurational
space is nontrivial. Here, we leverage a combination of machine learning
(ML) and first-principles calculations to enable and accelerate such
a targeted search. The ML models first screen for potentially superhard
B–C–O compositions from a large hypothetical B–C–O
candidate space. Atomic-level structure search using density functional
theory (DFT) within those identified compositions, followed by further
detailed analyses, unravels on four potentially superhard B–C–O
phases exhibiting thermodynamic, mechanical, and dynamic stability