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The energy landscape of magnetic materials

Louis Ponet1*, Enrico Di Lucente1*, Nicola Marzari1,2*

1 Theory and Simulation of Materials (THEOS), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

2 National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

* Corresponding authors emails: louis.ponet@epfl.ch, enrico.dilucente@epfl.ch, nicola.marzari@epfl.ch
DOI10.24435/materialscloud:14-b3 [version v1]

Publication date: May 23, 2024

How to cite this record

Louis Ponet, Enrico Di Lucente, Nicola Marzari, The energy landscape of magnetic materials, Materials Cloud Archive 2024.76 (2024), https://doi.org/10.24435/materialscloud:14-b3


Magnetic materials can display many solutions to the electronic-structure problem, corresponding to different local or global minima of the energy functional. In Hartree-Fock or density-functional theory different single-determinant solutions lead to different magnetizations, ionic oxidation states, hybridizations, and inter-site magnetic couplings. The vast majority of these states can be fingerprinted through their projection on the atomic orbitals of the magnetic ions. We have devised an approach that provides an effective control over these occupation matrices, allowing us to systematically explore the landscape of the potential energy surface. We showcase the emergence of a complex zoology of self-consistent states; even more so when semi-local density-functional theory is augmented - and typically made more accurate - by Hubbard corrections. Such extensive explorations allow to robustly identify the ground state of magnetic systems, and to assess the accuracy (or not) of current functionals and approximations

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File name Size Description
1.6 GiB Compressed tarball, has NiO, Ni2O2, Ni4O4 as folders. Each folder contains a report and job_backups, the latter of which contains the input and output files created for each of the trial jobs.


Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
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External references

L. Ponet, E. Di Lucente, N. Marzari, npj computational materials doi:https://doi.org/10.21203/rs.3.rs-3358581/v1


high-throughput density-functional theory OpenModel MARVEL

Version history:

2024.76 (version v1) [This version] May 23, 2024 DOI10.24435/materialscloud:14-b3