Author ORCID Identifier
https://orcid.org/0000-0001-5238-3689
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https://orcid.org/0000-0002-4324-0106
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Document Type
Article
Publication Date
11-11-2021
College/Unit
Eberly College of Arts and Sciences
Department/Program/Center
Physics and Astronomy
Abstract
The density-functional theory is widely used to predict the physical properties of materials. However, it usually fails for strongly correlated materials. A popular solution is to use the Hubbard correction to treat strongly correlated electronic states. Unfortunately, the values of the Hubbard U and J parameters are initially unknown, and they can vary from one material to another. In this semi-empirical study, we explore the U and J parameter space of a group of iron-based compounds to simultaneously improve the prediction of physical properties (volume, magnetic moment, and bandgap). We used a Bayesian calibration assisted by Markov chain Monte Carlo sampling for three different exchange-correlation functionals (LDA, PBE, and PBEsol). We found that LDA requires the largest U correction. PBE has the smallest standard deviation and its U and J parameters are the most transferable to other iron-based compounds. Lastly, PBE predicts lattice parameters reasonably well without the Hubbard correction.
Digital Commons Citation
Tavadze, Pedram; Boucher, Reese; Avendaño-Franco, Guillermo; Kocan, Keenan X.; Singh, Sobhit; Dovale-Farelo, Viviana; Ibarra-Hernández, Wilfredo; Johnson, Matthew B.; Mebane, David S.; and Romero, Aldo H., "Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling" (2021). Faculty & Staff Scholarship. 3060.
https://researchrepository.wvu.edu/faculty_publications/3060
Source Citation
Tavadze, P., Boucher, R., Avendaño-Franco, G. et al. Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling. npj Comput Mater 7, 182 (2021). https://doi.org/10.1038/s41524-021-00651-0
Comments
© 2021 Tavadze et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
This article received support from the WVU Libraries' Open Access Author Fund.