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dc.contributor.authorTantardini, Christian
dc.contributor.authorZakaryan, Hayk A.
dc.contributor.authorHan, Zhong-Kang
dc.contributor.authorAltalhi, Tariq
dc.contributor.authorLevchenko, Sergey V.
dc.contributor.authorKvashnin, Alexander G.
dc.contributor.authorYakobson, Boris I.
dc.date.accessioned2024-11-05T14:27:15Z
dc.date.available2024-11-05T14:27:15Z
dc.date.issued2024-08-06
dc.description.abstractHardness is a materials’ property with implications in several industrial fields, including oil and gas, manufacturing, and others. However, the relationship between this macroscale property and atomic (i.e., microscale) properties is unknown and in the last decade several models have unsuccessfully tried to correlate them in a wide range of chemical space. The understanding of such relationship is of fundamental importance for discovery of harder materials with specific characteristics to be employed in a wide range of fields. In this work, we have found a physical descriptor for Vickers hardness using a symbolic-regression artificial-intelligence approach based on compressed sensing. SISSO (Sure Independence Screening plus Sparsifying Operator) is an artificial-intelligence algorithm used for discovering simple and interpretable predictive models. It performs feature selection from up to billions of candidates obtained from several primary features by applying a set of mathematical operators. The resulting sparse SISSO model accurately describes the target property (i.e., Vickers hardness) with minimal complexity. We have considered the experimental values of hardness for binary, ternary, and quaternary transition-metal borides, carbides, nitrides, carbonitrides, carboborides, and boronitrides of 61 materials, on which the fitting was performed.. The found descriptor is a non-linear function of the microscopic properties, with the most significant contribution being from a combination of Voigt-averaged bulk modulus, Poisson’s ratio, and Reuss-averaged shear modulus. Results of high-throughput screening of 635 candidate materials using the found descriptor suggest the enhancement of material’s hardness through mixing with harder yet metastable structures (e.g., metastable VN, TaN, ReN<sub>2</suB, Cr<sub>3</sub>N<sub>4</sub>, and ZrB<sub>6</sub> all exhibit high hardness).en_US
dc.identifier.citationTantardini, Zakaryan, Han, Altalhi, Levchenko, Kvashnin, Yakobson. Material hardness descriptor derived by symbolic regression. Journal of Computational Science. 2024;82
dc.identifier.cristinIDFRIDAID 2298362
dc.identifier.doi10.1016/j.jocs.2024.102402
dc.identifier.issn1877-7503
dc.identifier.issn1877-7511
dc.identifier.urihttps://hdl.handle.net/10037/35459
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalJournal of Computational Science
dc.relation.projectIDSigma2: nn14654k
dc.relation.projectIDNorges forskningsråd: 324590
dc.relation.projectIDNorges forskningsråd: 262695
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleMaterial hardness descriptor derived by symbolic regressionen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution 4.0 International (CC BY 4.0)