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Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling

Pedram Tavadze1*, Reese Boucher1*, Guillermo Avendaño-Franco1*, Keenan X. Kocan2*, Sobhit Singh3*, Viviana Dovale-Farelo1*, Wilfredo Ibarra-Hernández4*, Matthew B Johnson1*, David S. Mebane2*, Aldo H Romero1*

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), doi: 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

No Explore or Discover sections associated with this archive record.

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.

Keywords

DFT+U Markov chain Monte Carlo MCMC Bayesian calibration

Version history:

2021.188 (version v1) [This version] Nov 03, 2021 DOI10.24435/materialscloud:16-d6