Mukherjee M, Sahu H, Losego MD, Gutekunst WR, Ramprasad R. Informatics-Driven Design of Superhard B-C-O Compounds.
ACS APPLIED MATERIALS & INTERFACES 2024;
16:10372-10379. [PMID:
38367252 PMCID:
PMC10910474 DOI:
10.1021/acsami.3c18105]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/19/2024]
Abstract
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.
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