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dc.contributor.authorSanvito, Stefano
dc.contributor.authorPatil, Urvesh
dc.contributor.authorDomina, Michelangelo
dc.date.accessioned2024-03-25T17:30:33Z
dc.date.available2024-03-25T17:30:33Z
dc.date.issued2023
dc.date.submitted2023en
dc.identifier.citationBruno Focassio, Michelangelo Domina, Urvesh Patil, Adalberto Fazzio, Stefano Sanvito, Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations, npj Computational Materials, 9, 1, 2023en
dc.identifier.otherY
dc.identifier.urihttp://hdl.handle.net/2262/107829
dc.descriptionPUBLISHEDen
dc.description.abstractAs the go-to method to solve the electronic structure problem, Kohn-Sham density functional theory (KS-DFT) can be used to obtain the ground-state charge density, total energy, and several other key materials’ properties. Unfortunately, the solution of the Kohn-Sham equations is found iteratively. This is a numerically intensive task, limiting the possible size and complexity of the systems to be treated. Machine-learning (ML) models for the charge density can then be used as surrogates to generate the converged charge density and reduce the computational cost of solving the electronic structure problem. We derive a powerful grid-centred structural representation based on the Jacobi and Legendre polynomials that, combined with a linear regression built on a dataefficient workflow, can accurately learn the charge density. Then, we design a machine-learning pipeline that can return energy and forces at the quality of a converged DFT calculation but at a fraction of the computational cost. This can be used as a tool for the fast scanning of the energy landscape and as a starting point to the DFT self-consistent cycle, in both cases maintaining a low computational cost.en
dc.language.isoenen
dc.relation.ispartofseriesnpj Computational Materials;
dc.relation.ispartofseries9;
dc.relation.ispartofseries1;
dc.rightsYen
dc.titleLinear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculationsen
dc.typeJournal Articleen
dc.type.supercollectionscholarly_publicationsen
dc.type.supercollectionrefereed_publicationsen
dc.identifier.peoplefinderurlhttp://people.tcd.ie/sanvitos
dc.identifier.peoplefinderurlhttp://people.tcd.ie/patilu
dc.identifier.peoplefinderurlhttp://people.tcd.ie/dominam
dc.identifier.rssinternalid263287
dc.identifier.doihttps://doi.org/10.1038/s41524-023-01053-0
dc.identifier.doihttps://doi.org/10.48550/arXiv.2301.13550
dc.rights.ecaccessrightsopenAccess
dc.subject.TCDThemeNanoscience & Materialsen
dc.identifier.orcid_id0000-0002-0291-715X


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