1. Exploring DFT+U parameter space with a Bayesian calibration assisted by Markov chain Monte Carlo sampling.
- Author
-
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.
- Subjects
DENSITY functional theory ,MARKOV chain Monte Carlo ,LATTICE theory ,CALIBRATION ,PARAMETER estimation - 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF