Pedram Tavadze^1*, Reese Boucher^1*, Guillermo Avendaño-Franco^1*, Keenan X. Kocan^2*, Sobhit Singh^3*, Viviana Dovale-Farelo^1*, Wilfredo Ibarra-Hernández^4*, Matthew B Johnson^1*, David S. Mebane^2*, Aldo H Romero^1*

1 Department of Physics and Astronomy, West Virginia University, Morgantown, WV, USA

2 Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, USA

3 Department of Physics and Astronomy, Rutgers University, Piscataway, NJ, USA

4 Facultad de Ingeniería, Benemérita Universidad Autónoma de Puebla, Apdo. Postal J-39, Puebla, Pue. 72570, México

* Corresponding authors emails: petavazohi@mix.wvu.edu, reb0019@mix.wvu.edu, guavendanofranco@mail.wvu.edu, kxkocan@mix.wvu.edu, ss3267@physics.rutgers.edu, vd0020@mix.wvu.edu, wilfredo.ibarra@correo.buap.mx, matthew.johnson@mail.wvu.edu, David.Mebane@mail.wvu.edu, aldo.romero@mail.wvu.edu

DOI10.24435/materialscloud:16-d6 [version v1]

Publication date: Nov 03, 2021

How to cite this record

Pedram Tavadze, Reese Boucher, Guillermo Avendaño-Franco, Keenan X. Kocan, Sobhit Singh, Viviana Dovale-Farelo, Wilfredo Ibarra-Hernández, Matthew B Johnson, David S. Mebane, Aldo H Romero, Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling, Materials Cloud Archive 2021.188 (2021), https://doi.org/10.24435/materialscloud:16-d6

Description

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 corrections to treat strongly correlated electronic states. Unfortunately, the exact 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.

Materials Cloud sections using this data

Files

File name	Size	Description
README.txt MD5md5:9c33f83b5fb86466f00e35601b7dc7f6	1.1 KiB	README.txt
DataAvailnpj.tar.gz MD5md5:c948234392cbfebd4826319eb43d6eac	3.7 MiB	Compressed file contains 4 main directories, 1.distribution: The probability density generated from exploring the U and J parameter space using the Bayesian calibration assisted by a Markov chain Monte Carlo 2.evaluation_stage: Performance of the (U,J) from MCMC for the evaluation (FeO, α−Fe2O3, AlFeB2, Fe5PB2, Fe5SiB2) 3.exploration_stage: Performance of the (U,J) from MCMC for the exploration (Fe, Fe2P, Fe3Ge, BaFeO3, SrFeO3) 4.U3.8_J0.7: Performance of the (U,J) from Sasioglu et al PRB 2011.

License

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

External references

Keywords

DFT+U Markov chain Monte Carlo MCMC Bayesian calibration