Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling
Abstract
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.
- Authors:
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1829908
- Resource Type:
- Published Article
- Journal Name:
- npj Computational Materials
- Additional Journal Information:
- Journal Name: npj Computational Materials Journal Volume: 7 Journal Issue: 1; Journal ID: ISSN 2057-3960
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
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. United Kingdom: N. p., 2021.
Web. doi:10.1038/s41524-021-00651-0.
Tavadze, Pedram, Boucher, Reese, Avendaño-Franco, Guillermo, Kocan, Keenan X., Singh, Sobhit, Dovale-Farelo, Viviana, Ibarra-Hernández, Wilfredo, Johnson, Matthew B., Mebane, David S., & Romero, Aldo H. Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling. United Kingdom. https://doi.org/10.1038/s41524-021-00651-0
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. Thu .
"Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling". United Kingdom. https://doi.org/10.1038/s41524-021-00651-0.
@article{osti_1829908,
title = {Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling},
author = {Tavadze, Pedram and Boucher, Reese and Avendaño-Franco, Guillermo and Kocan, Keenan X. and Singh, Sobhit and Dovale-Farelo, Viviana and Ibarra-Hernández, Wilfredo and Johnson, Matthew B. and Mebane, David S. and Romero, Aldo H.},
abstractNote = {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.},
doi = {10.1038/s41524-021-00651-0},
journal = {npj Computational Materials},
number = 1,
volume = 7,
place = {United Kingdom},
year = {2021},
month = {11}
}
https://doi.org/10.1038/s41524-021-00651-0
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