Material hardness descriptor derived by symbolic regression

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dc.citation.articleNumber102402en_US
dc.citation.journalTitleJournal of Computational Scienceen_US
dc.citation.volumeNumber82en_US
dc.contributor.authorTantardini, Christianen_US
dc.contributor.authorZakaryan, Hayk A.en_US
dc.contributor.authorHan, Zhong-Kangen_US
dc.contributor.authorAltalhi, Tariqen_US
dc.contributor.authorLevchenko, Sergey V.en_US
dc.contributor.authorKvashnin, Alexander G.en_US
dc.contributor.authorYakobson, Boris I.en_US
dc.date.accessioned2024-10-01T14:03:56Zen_US
dc.date.available2024-10-01T14:03:56Zen_US
dc.date.issued2024en_US
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, ReN2, Cr3N4, and ZrB6 all exhibit high hardness).en_US
dc.identifier.citationTantardini, C., Zakaryan, H. A., Han, Z.-K., Altalhi, T., Levchenko, S. V., Kvashnin, A. G., & Yakobson, B. I. (2024). Material hardness descriptor derived by symbolic regression. Journal of Computational Science, 82, 102402. https://doi.org/10.1016/j.jocs.2024.102402en_US
dc.identifier.digital1-s20-S1877750324001959-mainen_US
dc.identifier.doihttps://doi.org/10.1016/j.jocs.2024.102402en_US
dc.identifier.urihttps://hdl.handle.net/1911/117887en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleMaterial hardness descriptor derived by symbolic regressionen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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