<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Mukherjee M</submitter><funding>Office of Naval Research</funding><pagination>10372-10379</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC10910474</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>16(8)</volume><pubmed_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</pubmed_abstract><journal>ACS applied materials &amp; interfaces</journal><pubmed_title>Informatics-Driven Design of Superhard B-C-O Compounds.</pubmed_title><pmcid>PMC10910474</pmcid><funding_grant_id>N00014-21-1-2258</funding_grant_id><pubmed_authors>Gutekunst WR</pubmed_authors><pubmed_authors>Mukherjee M</pubmed_authors><pubmed_authors>Sahu H</pubmed_authors><pubmed_authors>Losego MD</pubmed_authors><pubmed_authors>Ramprasad R</pubmed_authors></additional><is_claimable>false</is_claimable><name>Informatics-Driven Design of Superhard B-C-O Compounds.</name><description>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</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Feb</publication><modification>2025-04-05T11:38:30.109Z</modification><creation>2025-04-05T11:38:30.109Z</creation></dates><accession>S-EPMC10910474</accession><cross_references><pubmed>38367252</pubmed><doi>10.1021/acsami.3c18105</doi></cross_references></HashMap>