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

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

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Article in ACS applied materials & interfaces, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Madhubanti MukherjeeSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID 0000-0002-9259-6803

Harikrishna SahuSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID 0000-0001-5458-9488

Mark D LosegoSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID 0000-0002-9810-9834

Will R GutekunstSchool of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.

Rampi RamprasadSchool of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.ORCID 0000-0003-4630-1565

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

B−C−O chemical spacecrystal structure searchDFTelastic modulimachine learning (ML)superhardVicker’s hardness

Identifiers

PMID38367252

DOI10.1021/acsami.3c18105

PMCPMC10910474

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.