Martin Uhrin^1*, Austin Zadoks^2*, Luca Binci^2,3,4*, Nicola Marzari^2,5*, Iurii Timrov^2,5*

1 Université Grenoble Alpes, 1130 Rue de la Piscine, BP 75, 38402 St Martin D'Heres, France

2 Theory and Simulation of Materials (THEOS), and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), Ecole Polytechnique Federale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland

3 Department of Materials Science and Engineering, University of California at Berkeley, Berkeley, California 94720, United States

4 Materials Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

5 Laboratory for Materials Simulations (LMS), Paul Scherrer Institut (PSI), CH-5232 Villigen PSI, Switzerland

* Corresponding authors emails: martin.uhrin.10@ucl.ac.uk, austin.zadoks@epfl.ch, lbinci@berkeley.edu, nicola.marzari@epfl.ch, iurii.timrov@psi.ch

DOI10.24435/materialscloud:r5-42 [version v1]

Publication date: Oct 15, 2024

How to cite this record

Martin Uhrin, Austin Zadoks, Luca Binci, Nicola Marzari, Iurii Timrov, Dataset of self-consistent Hubbard parameters for Ni, Mn and Fe from linear-response, Materials Cloud Archive 2024.160 (2024), https://doi.org/10.24435/materialscloud:r5-42

Description

Density-functional theory with extended Hubbard functionals (DFT+U+V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to semi-local functionals which are particularly pronounced in systems with partially-filled d and f electronic states. However, achieving accuracy in this approach hinges upon the accurate determination of the on-site U and inter-site V Hubbard parameters. In practice, these are obtained either by semi-empirical tuning, requiring prior knowledge, or, more correctly, by using predictive but expensive first-principles calculations. This archive entry contains Hubbard parameters, occupation matrices and other data calculated for 28 materials and covers all steps in a self-consistent procedure where, at each step new Hubbard parameters are obtained via linear-response, a process that is repeated until the parameters no longer change. The primary purpose of this dataset is to support the development and validation of machine learning models that can be used to predict Hubbard parameters, sidestepping the need for expensive ab-initio density functional perturbation theory calculations.

Materials Cloud sections using this data

Files

File name	Size	Description
data_uv_2024_1_25.arrow MD5md5:98731288955f4e0bf13abfd6e03d54ed	68.0 MiB	Pandas dataframe store in feature format
README.md MD5md5:2d89b806e4fdfa65186afe82263ffd83	4.3 KiB	Gives details on what the dataset contains and how to load it in python

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution Share Alike 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

machine learning Hubbard corrections density-functional theory MARVEL