Work

Informatics-Driven Design of Superhard B–C–O Compounds

Year: 2024

Type: article

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.

Source: ACS Applied Materials & Interfaces

DOI: 10.1021/acsami.3c18105

Authors: Madhubanti Mukherjee, Harikrishna Sahu, Mark D. Losego, Will R. Gutekunst, Rampi Ramprasad

Institution: Georgia Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,500

Funder: Office of Naval Research